82 results on '"NG, MICHAEL"'
Search Results
2. On Reinforcement Learning in Stabilizability of Probabilistic Boolean Control Networks
- Author
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Lin, Lin, primary, Lam, James, additional, Shi, Peng, additional, Ng, Michael K., additional, and Lam, Hak-Keung, additional
- Published
- 2023
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3. Iterative Refinement for Multi-Source Visual Domain Adaptation (Extended abstract)
- Author
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Wu, Hanrui, primary, Yan, Yuguang, additional, Lin, Guosheng, additional, Yang, Min, additional, Ng, Michael K., additional, and Wu, Qingyao, additional
- Published
- 2023
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- View/download PDF
4. Transferable Feature Selection for Unsupervised Domain Adaptation : Extended Abstract
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Yan, Yuguang, primary, Wu, Hanrui, additional, Ye, Yuzhong, additional, Bi, Chaoyang, additional, Lu, Min, additional, Liu, Dapeng, additional, Wu, Qingyao, additional, and Ng, Michael K., additional
- Published
- 2023
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5. SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition
- Author
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Gao, Yihang, primary, Cheung, Ka Chun, additional, and Ng, Michael K., additional
- Published
- 2022
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6. Multiscale Feature Tensor Train Rank Minimization for Multidimensional Image Recovery.
- Author
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Zhang, Hao, Zhao, Xi-Le, Jiang, Tai-Xiang, Ng, Michael K., and Huang, Ting-Zhu
- Abstract
The general tensor-based methods can recover missing values of multidimensional images by exploiting the low-rankness on the pixel level. However, especially when considerable pixels of an image are missing, the low-rankness is not reliable on the pixel level, resulting in some details losing in their results, which hinders the performance of subsequent image applications (e.g., image recognition and segmentation). In this article, we suggest a novel multiscale feature (MSF) tensorization by exploiting the MSFs of multidimensional images, which not only helps to recover the missing values on a higher level, that is, the feature level but also benefits subsequent image applications. By exploiting the low-rankness of the resulting MSF tensor constructed by the new tensorization, we propose the convex and nonconvex MSF tensor train rank minimization (MSF-TT) to conjointly recover the MSF tensor and the corresponding original tensor in a unified framework. We develop the alternating directional method of multipliers (ADMMs) to solve the convex MSF-TT and the proximal alternating minimization (PAM) to solve the nonconvex MSF-TT. Moreover, we establish the theoretical guarantee of convergence for the PAM algorithm. Numerical examples of real-world multidimensional images show that the proposed MSF-TT outperforms other compared approaches in image recovery and the recovered MSF tensor can benefit the subsequent image recognition. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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- View/download PDF
7. Transferable Feature Selection for Unsupervised Domain Adaptation.
- Author
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Yan, Yuguang, Wu, Hanrui, Ye, Yuzhong, Bi, Chaoyang, Lu, Min, Liu, Dapeng, Wu, Qingyao, and Ng, Michael K.
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FEATURE selection ,MATHEMATICAL optimization ,FEATURE extraction - Abstract
Domain adaptation aims at extracting knowledge from auxiliary source domains to assist the learning task in a target domain. In classification problems, since the distributions of the source and target domains are different, directly using source data to build a classifier for the target domain may hamper the classification performance on the target data. Fortunately, in many tasks, there can be some features that are transferable, i.e., the source and target domains share similar properties. On the other hand, it is common that the source data contain noisy features which may degrade the learning performance in the target domain. This issue, however, is barely studied in existing works. In this paper, we propose to find a feature subset that is transferable across the source and target domains. As a result, the domain discrepancy measured on the selected features can be reduced. Moreover, we seek to find the most discriminative features for classification. To achieve the above goals, we formulate a new sparse learning model that is able to jointly reduce the domain discrepancy and select informative features for classification. We develop two optimization algorithms to address the derived learning problem. Extensive experiments on real-world data sets demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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8. A Generalized Framework for Edge-Preserving and Structure-Preserving Image Smoothing.
- Author
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Liu, Wei, Zhang, Pingping, Lei, Yinjie, Huang, Xiaolin, Yang, Jie, and Ng, Michael
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COMPUTER vision ,COMPUTER graphics ,IMAGE intensifiers ,APPLICATION software - Abstract
Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various requirements of different applications. In this paper, we first introduce the truncated Huber penalty function which shows strong flexibility under different parameter settings. A generalized framework is then proposed with the introduced truncated Huber penalty function. When combined with its strong flexibility, our framework is able to achieve diverse smoothing natures where contradictive smoothing behaviors can even be achieved. It can also yield the smoothing behavior that can seldom be achieved by previous methods, and superior performance is thus achieved in challenging cases. These together enable our framework capable of a range of applications and able to outperform the state-of-the-art approaches in several tasks, such as image detail enhancement, clip-art compression artifacts removal, guided depth map restoration, image texture removal, etc. In addition, an efficient numerical solution is provided and its convergence is theoretically guaranteed even the optimization framework is non-convex and non-smooth. A simple yet effective approach is further proposed to reduce the computational cost of our method while maintaining its performance. The effectiveness and superior performance of our approach are validated through comprehensive experiments in a range of applications. Our code is available at https://github.com/wliusjtu/Generalized-Smoothing-Framework . [ABSTRACT FROM AUTHOR]
- Published
- 2022
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9. A Tensor-Based Markov Chain Model for Heterogeneous Information Network Collective Classification.
- Author
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Han, Chao, Chen, Jian, Tan, Mingkui, Ng, Michael K., and Wu, Qingyao
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INFORMATION networks ,INFORMATION modeling ,MARKOV processes ,DISTRIBUTION (Probability theory) ,STATIONARY processes ,CLASSIFICATION - Abstract
Heterogeneous Information Network (HIN) collecitve classification studies the problem of predicting labels for one type of nodes in a HIN which contains multiple types of nodes multiple types of links among them. Previous studies have revealed that exploiting relative importance of links is quite useful to improve node classification performance as connected nodes tend to have similar labels. Most existing approaches exploit the relative importance of links either by directly counting the number of connections among nodes or by learning the weight of each type of link from labeled data only. However, these approaches either neglect the importance of types of links to the class labels or may lead to overfitting problem. We propose a Tensor-based Markov chain (T-Mark) approach, which is able to automatically and simultaneously predict the labels for unlabeled nodes and give the relative importance of types of links that actually improve the classification accuracy. Specifically, we build two tensor equations by using the HIN and features of nodes from both labeled and unlabeled data. A Markov chain-based model is proposed and it is solved by an iterative process to obtain the stationary distributions. Theoretical analyses of the existence and uniqueness of such probability distributions are given. Extensive experimental results demonstrate that T-Mark is able to achieve superior performance in the comparison and obtain reasonable relative importance of links. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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10. Low Rank Tensor Completion With Poisson Observations.
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Zhang, Xiongjun and Ng, Michael K.
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POISSON distribution , *UNITARY transformations , *VIDEO processing , *COMPUTER vision , *MATRIX decomposition , *MAXIMUM likelihood statistics - Abstract
Poisson observations for videos are important models in video processing and computer vision. In this paper, we study the third-order tensor completion problem with Poisson observations. The main aim is to recover a tensor based on a small number of its Poisson observation entries. A existing matrix-based method may be applied to this problem via the matricized version of the tensor. However, this method does not leverage on the global low-rankness of a tensor and may be substantially suboptimal. Our approach is to consider the maximum likelihood estimate of the Poisson distribution, and utilize the Kullback-Leibler divergence for the data-fitting term to measure the observations and the underlying tensor. Moreover, we propose to employ a transformed tensor nuclear norm ball constraint and a bounded constraint of each entry, where the transformed tensor nuclear norm is used to get a lower transformed multi-rank tensor with suitable unitary transformation matrices. We show that the upper bound of the error of the estimator of the proposed model is less than that of the existing matrix-based method. Also an information theoretic lower error bound is established. An alternating direction method of multipliers is developed to solve the resulting convex optimization model. Extensive numerical experiments on synthetic data and real-world datasets are presented to demonstrate the effectiveness of our proposed model compared with existing tensor completion methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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11. Enhanced Supervised Descent Learning Technique for Electromagnetic Inverse Scattering Problems by the Deep Convolutional Neural Networks.
- Author
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Yao, He Ming, Guo, Rui, Li, Maokun, Jiang, Lijun, and Ng, Michael Kwok Po
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ARTIFICIAL neural networks ,CONVOLUTIONAL neural networks ,SUPERVISED learning ,ELECTROMAGNETIC wave scattering ,DEEP learning - Abstract
This work proposes a novel deep learning (DL) framework to solve the electromagnetic inverse scattering (EMIS) problems. The proposed framework integrates the complex-valued deep convolutional neural network (DConvNet) into the supervised descent method (SDM) to realize both off-line training and on-line “imaging” prediction for EMIS. The offline training consists of two parts: 1) DConvNet training: the training dataset is created, and the proposed DConvNet is trained to realize the EM forward process and 2) SDM training: the trained DConvNet is integrated into the SDM framework, and the average descent directions between the initial prediction and the true label of SDM iterative schemes are learned based on the same dataset in part 1). In the online step, the contrasts (permittivities) reconstruction of scatterers is realized by the SDM iteration process based on learned descent directions, while its forward process is achieved by the trained complex-valued DConvNet. Ultimately, this framework provides a new perspective to integrate the prior information into the EMIS solving process with the maintained accuracy. Unlike the conventional SDM, the novel proposed framework can significantly shorten the computation and realize the real-time imaging. Various numerical examples and discussions are provided to demonstrate the efficiency and accuracy of the proposed novel framework. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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12. Non-Local Robust Quaternion Matrix Completion for Large-Scale Color Image and Video Inpainting.
- Author
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Jia, Zhigang, Jin, Qiyu, Ng, Michael K., and Zhao, Xi-Le
- Subjects
QUATERNIONS ,MACHINE learning ,INPAINTING ,IMAGE color analysis ,SINGULAR value decomposition - Abstract
The image nonlocal self-similarity (NSS) prior refers to the fact that a local patch often has many nonlocal similar patches to it across the image and has been widely applied in many recently proposed machining learning algorithms for image processing. However, there is no theoretical analysis on its working principle in the literature. In this paper, we discover a potential causality between NSS and low-rank property of color images, which is also available to grey images. A new patch group based NSS prior scheme is proposed to learn explicit NSS models of natural color images. The numerical low-rank property of patched matrices is also rigorously proved. The NSS-based QMC algorithm computes an optimal low-rank approximation to the high-rank color image, resulting in high PSNR and SSIM measures and particularly the better visual quality. A new tensor NSS-based QMC method is also presented to solve the color video inpainting problem based on quaternion tensor representation. The numerical experiments on color images and videos indicate the advantages of NSS-based QMC over the state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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13. Self-Supervised Nonlinear Transform-Based Tensor Nuclear Norm for Multi-Dimensional Image Recovery.
- Author
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Luo, Yi-Si, Zhao, Xi-Le, Jiang, Tai-Xiang, Chang, Yi, Ng, Michael K., and Li, Chao
- Subjects
DISCRETE Fourier transforms - Abstract
Recently, transform-based tensor nuclear norm (TNN) minimization methods have received increasing attention for recovering third-order tensors in multi-dimensional imaging problems. The main idea of these methods is to perform the linear transform along the third mode of third-order tensors and then minimize the nuclear norm of frontal slices of the transformed tensor. The main aim of this paper is to propose a nonlinear multilayer neural network to learn a nonlinear transform by solely using the observed tensor in a self-supervised manner. The proposed network makes use of the low-rank representation of the transformed tensor and data-fitting between the observed tensor and the reconstructed tensor to learn the nonlinear transform. Extensive experimental results on different data and different tasks including tensor completion, background subtraction, robust tensor completion, and snapshot compressive imaging demonstrate the superior performance of the proposed method over state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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14. Nonlocal Self-Similarity-Based Hyperspectral Remote Sensing Image Denoising With 3-D Convolutional Neural Network.
- Author
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Wang, Zhicheng, Ng, Michael K., Zhuang, Lina, Gao, Lianru, and Zhang, Bing
- Subjects
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CONVOLUTIONAL neural networks , *IMAGE denoising , *REMOTE sensing , *THREE-dimensional imaging , *DEEP learning , *MACHINE learning - Abstract
Recently, deep-learning-based denoising methods for hyperspectral images (HSIs) have been comprehensively studied and achieved impressive performance because they can effectively extract complex and nonlinear image features. Compared with deep-learning-based methods, the nonlocal similarity-based denoising methods are more suitable for images containing edges or regular textures. We propose a powerful HSI denoising method, termed non-local 3-D convolutional neural network (NL-3DCNN), combining traditional machine learning and deep learning techniques. NL-3DCNN exploits the high spectral correlation of an HSI using subspace representation, and the corresponding representation coefficients are termed eigenimages. The high spatial correlation in eigenimages is exploited by grouping nonlocal similar patches, which are denoised by a 3-D convolutional neural network. The numerical and graphical denoising results of the simulated and real data show that the proposed method is superior to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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15. Experimental Study on Generative Adversarial Network for Precipitation Nowcasting.
- Author
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Luo, Chuyao, Li, Xutao, Ye, Yunming, Feng, Shanshan, and Ng, Michael K.
- Subjects
GENERATIVE adversarial networks ,RECURRENT neural networks ,CONVOLUTIONAL neural networks ,SPACE-based radar ,OPTICAL radar ,RADAR meteorology - Abstract
Precipitation nowcasting is an important task, which can be used in numerous applications. The key challenge of the task lies in radar echo map prediction. Previous studies leverage the convolutional recurrent neural network (ConvRNN) to address the problem. However, the approaches are built upon mean square losses, and the results tend to have inaccurate appearances, shapes, and positions for predictions. To alleviate this problem, we explore the idea of adversarial regularization and systematically compare four types of generative adversarial networks (GANs), which are the combinations of GAN/Wasserstein GAN (WGAN) and its multiscale version. Extensive experiments on a real-world radar dataset and four typical meteorological examples are conducted. The results validate the effectiveness of adversarial regularization. The developed models show superior performances over the existing prediction approaches in the majority of circumstances. Moreover, we find that the WGAN regularization often delivers better results than the GAN regularization due to its robustness, and the multiscale WGAN, in general, performs the best among all the methods. To reproduce the results, we release the source code at https://github.com/luochuyao/MultiScaleGAN and the test system at http://39.97.217.145:80/. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
16. Iterative Refinement for Multi-Source Visual Domain Adaptation.
- Author
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Wu, Hanrui, Yan, Yuguang, Lin, Guosheng, Yang, Min, Ng, Michael K., and Wu, Qingyao
- Subjects
VISUAL accommodation ,FEATURE selection ,PHYSIOLOGICAL adaptation ,KNOWLEDGE transfer ,VALUES (Ethics) - Abstract
One of the main challenges in multi-source domain adaptation is how to reduce the domain discrepancy between each source domain and a target domain, and then evaluate the domain relevance to determine how much knowledge should be transferred from different source domains to the target domain. However, most prior approaches barely consider both discrepancies and relevance among domains. In this paper, we propose an algorithm, called Iterative Refinement based on Feature Selection and the Wasserstein distance (IRFSW), to solve semi-supervised domain adaptation with multiple sources. Specifically, IRFSW aims to explore both the discrepancies and relevance among domains in an iterative learning procedure, which gradually refines the learning performance until the algorithm stops. In each iteration, for each source domain and the target domain, we develop a sparse model to select features in which the domain discrepancy and training loss are reduced simultaneously. Then a classifier is constructed with the selected features of the source and labeled target data. After that, we exploit optimal transport over the selected features to calculate the transferred weights. The weight values are taken as the ensemble weights to combine the learned classifiers to control the amount of knowledge transferred from source domains to the target domain. Experimental results validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
17. Tensorizing GAN With High-Order Pooling for Alzheimer’s Disease Assessment.
- Author
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Yu, Wen, Lei, Baiying, Ng, Michael K., Cheung, Albert C., Shen, Yanyan, and Wang, Shuqiang
- Subjects
ALZHEIMER'S disease ,DEEP learning ,GALLIUM nitride ,MAGNETIC resonance imaging ,MILD cognitive impairment ,EARLY diagnosis - Abstract
It is of great significance to apply deep learning for the early diagnosis of Alzheimer’s disease (AD). In this work, a novel tensorizing GAN with high-order pooling is proposed to assess mild cognitive impairment (MCI) and AD. By tensorizing a three-player cooperative game-based framework, the proposed model can benefit from the structural information of the brain. By incorporating the high-order pooling scheme into the classifier, the proposed model can make full use of the second-order statistics of holistic magnetic resonance imaging (MRI). To the best of our knowledge, the proposed Tensor-train, High-order pooling and Semisupervised learning-based GAN (THS-GAN) is the first work to deal with classification on MR images for AD diagnosis. Extensive experimental results on Alzheimer’s disease neuroimaging initiative (ADNI) data set are reported to demonstrate that the proposed THS-GAN achieves superior performance compared with existing methods, and to show that both tensor-train and high-order pooling can enhance classification performance. The visualization of generated samples also shows that the proposed model can generate plausible samples for semisupervised learning purpose. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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18. Hyperspectral Image Denoising Based on Global and Nonlocal Low-Rank Factorizations.
- Author
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Zhuang, Lina, Fu, Xiyou, Ng, Michael K., and Bioucas-Dias, Jose M.
- Subjects
SINGULAR value decomposition ,IMAGE denoising ,FACTORIZATION ,SIGNAL-to-noise ratio ,COMPUTATIONAL complexity - Abstract
The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio of the measurements, thus calling for effective denoising techniques. HSIs from the real world lie in low-dimensional subspaces and are self-similar. The low dimensionality stems from the high correlation existing among the reflectance vectors, and self-similarity is common in real-world images. In this article, we exploit the above two properties. The low dimensionality is a global property that enables the denoising to be formulated just with respect to the subspace representation coefficients, thus greatly improving the denoising performance and reducing the computational complexity during processing. The self-similarity is exploited via a low-rank tensor factorization of nonlocal similar 3-D patches. The proposed factorization hinges on the optimal shrinkage/thresholding of the singular value decomposition (SVD) singular values of low-rank tensor unfoldings. As a result, the proposed method is user friendly and insensitive to its parameters. Its effectiveness is illustrated in a comparison with state-of-the-art competitors. A MATLAB demo of this work is available at https://github.com/LinaZhuang for the sake of reproducibility. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
19. On Recovery of Sparse Signals With Prior Support Information via Weighted ℓ ₚ -Minimization.
- Author
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Ge, Huanmin, Chen, Wengu, and Ng, Michael K.
- Subjects
CLASSICAL conditioning ,WASTE minimization ,NOISE measurement - Abstract
A complete characterization for the restricted isometry constant (RIC) bounds on $\delta _{{{ tk}}}$ for all $ {t}>0$ is an important problem on recovery of sparse signals with prior support information via weighted $\ell _{{p}}$ -minimization ($0 < {p} \leqslant 1$). In this paper, new bounds on the restricted isometry constants $\delta _{{{ tk}}}$ ($0 < {t} < \frac {4}{3}{d}$), where $d$ is a key constant determined by prior support information, are established to guarantee the sparse signal recovery via the weighted $\ell _{{p}}$ minimization in both noiseless and noisy settings. This result fills a vacancy on $\delta _{{{ tk}}}$ with $0 < {t} < \frac {4}{3}{d}$ , compared with previous works on $\delta _{{{ tk}}}$ (${t} \geqslant \frac {4}3{d}$). We show that, when the accuracy of prior support estimate is at least 50%, the new recovery condition in terms of $\delta _{{{ tk}}}$ ($0 < {t} < \frac {4}{3}{d}$) via weighted $\ell _{1}$ minimization is weaker than the condition required by classical $\ell _{1}$ minimization without weighting. Our weighted $\ell _{1}$ minimization gives better recovery error bounds in noisy setting. Similarly, the new recovery condition in terms of $\delta _{{{ tk}}}$ ($0 < {t} < \frac {4}{3}{d}$) is extended to weighted $\ell _{{p}}$ ($0 < {p} < 1$) minimization, and it is also weaker than the condition obtained by standard non-convex $\ell _{{p}}$ ($0 < {p} < 1$) minimization without weighting. Numerical illustrations are provided to demonstrate our new theoretical results. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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20. Cross-track Illumination Correction For Hyperspectral Pushbroom Sensors Using Total Variation and Sparsity Regularization
- Author
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Zhuang, Lina, primary and Ng, Michael K., additional
- Published
- 2020
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21. A Weighted Tensor Factorization Method for Low-Rank Tensor Completion
- Author
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Cheng, Miaomiao, primary, Jing, Liping, additional, and Ng, Michael K., additional
- Published
- 2019
- Full Text
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22. Heterogeneous Domain Adaptation by Information Capturing and Distribution Matching.
- Author
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Wu, Hanrui, Zhu, Hong, Yan, Yuguang, Wu, Jiaju, Zhang, Yifan, and Ng, Michael K.
- Subjects
IMAGE recognition (Computer vision) ,ORTHOGRAPHIC projection ,INFORMATION measurement ,IMAGE reconstruction ,INFORMATION resources - Abstract
Heterogeneous domain adaptation (HDA) is a challenging problem because of the different feature representations in the source and target domains. Most HDA methods search for mapping matrices from the source and target domains to discover latent features for learning. However, these methods barely consider the reconstruction error to measure the information loss during the mapping procedure. In this paper, we propose to jointly capture the information and match the source and target domain distributions in the latent feature space. In the learning model, we propose to minimize the reconstruction loss between the original and reconstructed representations to preserve information during transformation and reduce the Maximum Mean Discrepancy between the source and target domains to align their distributions. The resulting minimization problem involves two projection variables with orthogonal constraints that can be solved by the generalized gradient flow method, which can preserve orthogonal constraints in the computational procedure. We conduct extensive experiments on several image classification datasets to demonstrate that the effectiveness and efficiency of the proposed method are better than those of state-of-the-art HDA methods. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
23. Multi-Dimensional Visual Data Completion via Low-Rank Tensor Representation Under Coupled Transform.
- Author
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Wang, Jian-Li, Huang, Ting-Zhu, Zhao, Xi-Le, Jiang, Tai-Xiang, and Ng, Michael K.
- Subjects
DISCRETE Fourier transforms ,MULTISPECTRAL imaging ,FOURIER transforms ,SINGULAR value decomposition - Abstract
This paper addresses the tensor completion problem, which aims to recover missing information of multi-dimensional images. How to represent a low-rank structure embedded in the underlying data is the key issue in tensor completion. In this work, we suggest a novel low-rank tensor representation based on coupled transform, which fully exploits the spatial multi-scale nature and redundancy in spatial and spectral/temporal dimensions, leading to a better low tensor multi-rank approximation. More precisely, this representation is achieved by using two-dimensional framelet transform for the two spatial dimensions, one/two-dimensional Fourier transform for the temporal/spectral dimension, and then Karhunen–Loéve transform (via singular value decomposition) for the transformed tensor. Based on this low-rank tensor representation, we formulate a novel low-rank tensor completion model for recovering missing information in multi-dimensional visual data, which leads to a convex optimization problem. To tackle the proposed model, we develop the alternating directional method of multipliers (ADMM) algorithm tailored for the structured optimization problem. Numerical examples on color images, multispectral images, and videos illustrate that the proposed method outperforms many state-of-the-art methods in qualitative and quantitative aspects. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
24. Constrained Low-Tubal-Rank Tensor Recovery for Hyperspectral Images Mixed Noise Removal by Bilateral Random Projections
- Author
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Zhang, Hao, primary, Zhao, Xi-Le, additional, Jiang, Tai-Xiang, additional, and Ng, Michael Kwok-Po, additional
- Published
- 2019
- Full Text
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25. Structured Convex Optimization Method for Orthogonal Nonnegative Matrix Factorization
- Author
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Pan, Junjun, primary, Ng, Michael K., additional, and Zhang, Xiongjun, additional
- Published
- 2018
- Full Text
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26. Structured Dictionary Learning for Image Denoising Under Mixed Gaussian and Impulse Noise.
- Author
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Zhu, Hong and Ng, Michael K.
- Subjects
- *
BURST noise , *RANDOM noise theory , *IMAGE reconstruction , *IMAGE denoising , *NOISE control , *NOISE - Abstract
Although image denoising as a basic task of image restoration has been widely studied in the past decades, there are not many studies on mixed noise denoising. In this paper, we propose two structured dictionary learning models to recover images corrupted by mixed Gaussian and impulse noise. These two models can be merged as $\ell _{p}$ -norm fidelity plus $\ell _{q}$ -norm regularization. The fidelity term is used to fit image patches and the regularization term is employed for sparse coding. Particularly, we utilize proximal (and proximal linearized) alternating minimization methods as the main solvers to deal with these two models. We remove the Gaussian noise under the assumption that the uncorrupted image can be approximated with a linear representation under an appropriate orthogonal basis. We use different ways to remove impulse noise for these two models. The experimental results are reported to compare the existing methods and demonstrate the performance of the proposed denoising model is better than the other existing methods in terms of some quality assessment metrics. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
27. Framelet Representation of Tensor Nuclear Norm for Third-Order Tensor Completion.
- Author
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Jiang, Tai-Xiang, Ng, Michael K., Zhao, Xi-Le, and Huang, Ting-Zhu
- Subjects
- *
SINGULAR value decomposition , *DISCRETE Fourier transforms , *MAGNETIC resonance imaging , *MULTISPECTRAL imaging , *FOURIER transforms , *MATRIX decomposition - Abstract
The main aim of this paper is to develop a framelet representation of the tensor nuclear norm for third-order tensor recovery. In the literature, the tensor nuclear norm can be computed by using tensor singular value decomposition based on the discrete Fourier transform matrix, and tensor completion can be performed by the minimization of the tensor nuclear norm which is the relaxation of the sum of matrix ranks from all Fourier transformed matrix frontal slices. These Fourier transformed matrix frontal slices are obtained by applying the discrete Fourier transform on the tubes of the original tensor. In this paper, we propose to employ the framelet representation of each tube so that a framelet transformed tensor can be constructed. Because of framelet basis redundancy, the representation of each tube is sparsely represented. When the matrix slices of the original tensor are highly correlated, we expect the corresponding sum of matrix ranks from all framelet transformed matrix frontal slices would be small, and the resulting tensor completion can be performed much better. The proposed minimization model is convex and global minimizers can be obtained. Numerical results on several types of multi-dimensional data (videos, multispectral images, and magnetic resonance imaging data) have tested and shown that the proposed method outperformed the other testing methods. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
28. 3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model.
- Author
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Zeng, Jin, Cheung, Gene, Ng, Michael, Pang, Jiahao, and Yang, Cheng
- Subjects
POINT cloud ,MATHEMATICAL regularization ,POINT processes ,LAPLACIAN matrices - Abstract
3D point cloud—a new signal representation of volumetric objects—is a discrete collection of triples marking exterior object surface locations in 3D space. Conventional imperfect acquisition processes of 3D point cloud—e.g., stereo-matching from multiple viewpoint images or depth data acquired directly from active light sensors—imply non-negligible noise in the data. In this paper, we extend a previously proposed low-dimensional manifold model for the image patches to surface patches in the point cloud, and seek self-similar patches to denoise them simultaneously using the patch manifold prior. Due to discrete observations of the patches on the manifold, we approximate the manifold dimension computation defined in the continuous domain with a patch-based graph Laplacian regularizer, and propose a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise. We show that our graph Laplacian regularizer leads to speedy implementation and has desirable numerical stability properties given its natural graph spectral interpretation. Extensive simulation results show that our proposed denoising scheme outperforms state-of-the-art methods in objective metrics and better preserves visually salient structural features like edges. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
29. New RIP Bounds for Recovery of Sparse Signals With Partial Support Information via Weighted ${\ell_{p}}$ -Minimization.
- Author
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Ge, Huanmin, Chen, Wengu, and Ng, Michael K.
- Subjects
ACCURACY of information ,NOISE measurement - Abstract
In this paper, we consider the recovery of $k$ -sparse signals using the weighted $\ell _{p}$ ($0< p \leqslant 1$) minimization when some partial prior information on the support is available. First, we present a unified analysis of restricted isometry constant $\delta _{tk}$ with $d< t \leqslant 2d$ ($d \geqslant 1$ is determined by the prior support information) for sparse signal recovery by the weighted $\ell _{p}$ ($0< p \leqslant 1$) minimization in both noiseless and noisy settings. This result fills a vacancy on $\delta _{tk}$ with $t< 2$ , compared with previous works on $\delta _{(a+1)k}$ ($a>1$). Second, we provide a sufficient condition on $\delta _{tk}$ with $1< t \leqslant 2$ for the recovery of sparse signals using the $\ell _{p}$ ($0< p \leqslant 1$) minimization, which extends the existing optimal result on $\delta _{2k}$ in the literature. Last, various numerical examples are presented to demonstrate the better performance of the weighted $\ell _{p}$ ($0< p \leqslant 1$) minimization is achieved when the accuracy of prior information on the support is at least 50%, compared with that of the $\ell _{p}$ ($0< p \leqslant 1$) minimization. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
30. Multi-Domain Networks Association for Biological Data Using Block Signed Graph Clustering.
- Author
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Liu, Ye, Ng, Michael K., and Wu, Stephen
- Abstract
Multi-domain biological network association and clustering have attracted a lot of attention in biological data integration and understanding, which can provide a more global and accurate understanding of biological phenomenon. In many problems, different domains may have different cluster structures. Due to rapid growth of data collection from different sources, some domains may be strongly or weakly associated with the other domains. A key challenge is how to determine the degree of association among different domains, and to achieve accurate clustering results by data integration. In this paper, we propose an unsupervised learning approach for multi-domain network association by using block signed graph clustering. In particular, with consistency weights calculation, the proposed algorithm automatically identify domains relevant to each other strongly (or weakly) by assigning them larger (or smaller) weights. This approach not only significantly improve clustering accuracy but also understand multi-domain networks association. In each iteration of the proposed algorithm, we update consistency weights based on cluster structure of each domain, and then make use of different sets of eigenvectors to obtain different cluster structures in each domain. Experimental results on both synthetic data sets and real data sets (including neuron activity data and gene expression data) empirically demonstrate the effectiveness of the proposed algorithm in clustering performance and in domain association capability. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
31. Structural Similarity-Based Nonlocal Variational Models for Image Restoration.
- Author
-
Wang, Wei, Li, Fang, and Ng, Michael K.
- Subjects
IMAGE reconstruction ,TECHNOLOGY convergence ,PIXELS ,IMAGE denoising ,MATHEMATICAL regularization ,INVERSE problems - Abstract
In this paper, we propose and develop a novel nonlocal variational technique based on structural similarity (SS) information for image restoration problems. In the literature, patches extracted from images are compared according to their pixel values, and then nonlocal filtering can be employed for image restoration. The disadvantage of this approach is that intensity-based patch distance may not be effective in image restoration, especially for images containing texture or structural information. The main aim of this paper is to propose using SS between image patches to develop nonlocal regularization models. In particular, two types of nonlocal regularizing functions are studied: an SS-based nonlocal quadratic function (SS-NLH1) and an SS-based nonlocal total variation function (SS-NLTV) for regularization of image restoration problems. Moreover, we employ iterative algorithms to solve these SS-NLH1 and SS-NLTV variational models numerically and discuss the convergence of these algorithms. The experimental results are presented to demonstrate the effectiveness of the proposed models. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
32. Topic-Adaptive Sentiment Lexicon Construction
- Author
-
Deng, Dong, primary, Jing, Liping, additional, Yu, Jian, additional, and Ng, Michael K., additional
- Published
- 2018
- Full Text
- View/download PDF
33. Tensor Based Relations Ranking for Multi-relational Collective Classification
- Author
-
Han, Chao, primary, Wu, Qingyao, additional, Ng, Michael K., additional, Cao, Jiezhang, additional, Tan, Mingkui, additional, and Chen, Jian, additional
- Published
- 2017
- Full Text
- View/download PDF
34. Multi-scale-and-depth convolutional neural network for remote sensed imagery pan-sharpening
- Author
-
Wei, Yancong, primary, Yuan, Qiangqiang, additional, Meng, Xiangchao, additional, Shen, Huanfeng, additional, Zhang, Liangpei, additional, and Ng, Michael, additional
- Published
- 2017
- Full Text
- View/download PDF
35. Miss data reconstruction in remote sensing images with a double weighted tensor low rank model
- Author
-
Yuan, Qiangqiang, primary, Ng, Michael, additional, Shen, Huanfeng, additional, Zhang, Liangpei, additional, and Li, Jie, additional
- Published
- 2017
- Full Text
- View/download PDF
36. Graph Spectral Image Processing.
- Author
-
Cheung, Gene, Magli, Enrico, Tanaka, Yuichi, and Ng, Michael K.
- Subjects
BRAIN mapping ,BRAIN imaging ,WIRELESS sensor networks ,BODY sensor networks ,IMAGE compression - Abstract
Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2-D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this paper, we overview recent graph spectral techniques in GSP specifically for image/video processing. The topics covered include image compression, image restoration, image filtering, and image segmentation. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
37. Multi-Label Classification by Semi-Supervised Singular Value Decomposition.
- Author
-
Jing, Liping, Yu, Jian, Shen, Chenyang, Ng, Michael K., and Yang, Liu
- Subjects
IMAGE analysis ,SINGULAR value decomposition ,ALGORITHMS ,CLASSIFICATION ,BIG data - Abstract
Multi-label problems arise in various domains, including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do not adequately address two key challenges: exploiting correlations between labels and making up for the lack of labelled data or even missing labelled data. In this paper, we proposed to use a semi-supervised singular value decomposition (SVD) to handle these two challenges. The proposed model takes advantage of the nuclear norm regularization on the SVD to effectively capture the label correlations. Meanwhile, it introduces manifold regularization on mapping to capture the intrinsic structure among data, which provides a good way to reduce the required labelled data with improving the classification performance. Furthermore, we designed an efficient algorithm to solve the proposed model based on the alternating direction method of multipliers, and thus, it can efficiently deal with large-scale data sets. Experimental results for synthetic and real-world multimedia data sets demonstrate that the proposed method can exploit the label correlations and obtain promising and better label prediction results than the state-of-the-art methods. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
38. A Semisupervised Classification Approach for Multidomain Networks With Domain Selection.
- Author
-
Chen, Chuan, Xin, Jingxue, Wang, Yong, Chen, Luonan, and Ng, Michael K.
- Subjects
BIOINFORMATICS ,MOLECULAR biology - Abstract
Multidomain network classification has attracted significant attention in data integration and machine learning, which can enhance network classification or prediction performance by integrating information from different sources. Despite the previous success, existing multidomain network learning methods usually assume that different views are available for the same set of instances, and thus, they seek a consistent classification result for all domains. However, in many real-world problems, each domain has its specific instance set, and one instance in one domain may correspond to multiple instances in another domain. Moreover, due to the rapid growth of data sources, different domains may not be relevant to each other, which asks for selecting domains relevant to the target/focused domain. A key challenge under this setting is how to achieve accurate prediction by integrating different data representations without losing data information. In this paper, we propose a semisupervised classification approach for a multidomain network based on label propagation, i.e., multidomain classification with domain selection (MCS), which can deal with the cross-domain information and different instance sets in domains. In particular, with sparse weight properties, the proposed MCS can automatically identify those domains relevant to our target domain by assigning them higher weights than the other irrelevant domains. This not only significantly improves a classification accuracy but also helps to obtain optimal network partition for the target domain. From the theoretical viewpoint, we equivalently decompose MCS into two simpler subproblems with analytical solutions, which can be efficiently solved by their computational procedures. Extensive experimental results on both synthetic and real-world data sets empirically demonstrate the advantages of the proposed approach in terms of both prediction performance and domain selection ability. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
39. A tensor-based Markov chain method for module identification from multiple networks
- Author
-
Shen, Chenyang, primary, Zhang, Shuqin, additional, and Ng, Michael K., additional
- Published
- 2014
- Full Text
- View/download PDF
40. An Adaptive Weighted Tensor Completion Method for the Recovery of Remote Sensing Images With Missing Data.
- Author
-
Ng, Michael Kwok-Po, Yuan, Qiangqiang, Yan, Li, and Sun, Jing
- Subjects
- *
REMOTE sensing , *TENSOR algebra , *IMAGE quality analysis , *DATA analysis , *SIGNAL-to-noise ratio , *MODIS (Spectroradiometer) - Abstract
Missing information, such as dead pixel values and cloud effects, is very common image quality degradation problems in remote sensing. Missing information can reduce the accuracy of the subsequent image processing, in applications such as classification, unmixing, and target detection, and even the quantitative retrieval process. The main aim of this paper is to study an adaptive weighted tensor completion (AWTC) method for the recovery of remote sensing images with missing data. Our idea is to collectively make use of the spatial, spectral, and temporal information to build a new weighted tensor low-rank regularization model for recovering the missing data. In the model, the weights are determined adaptively by considering the contribution of the spatial, spectral, and temporal information in each dimension. Experimental results based on both simulated and real data sets are presented to verify that the proposed method can recover missing data, and its performance is found to be better than the other tested methods. In the simulated experiments, the peak signal-to-noise ratio is improved by more than 3 dB, compared with the original tensor completion model. In the real data experiments, the proposed AWTC model can better recover the dead line problem in Aqua Moderate Resolution Imaging Spectroradiometer band 6 and the scan-line corrector-off problem in enhanced thematic mapper plus images, with the smallest spectral distortion. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
41. ML-FOREST: A Multi-Label Tree Ensemble Method for Multi-Label Classification.
- Author
-
Wu, Qingyao, Tan, Mingkui, Song, Hengjie, Chen, Jian, and Ng, Michael K.
- Subjects
BIOINFORMATICS ,COMPUTER vision ,ALGORITHMS ,LABELING theory ,GRAPH labelings - Abstract
Multi-label classification deals with the problem where each example is associated with multiple class labels. Since the labels are often dependent to other labels, exploiting label dependencies can significantly improve the multi-label classification performance. The label dependency in existing studies is often given as prior knowledge or learned from the labels only. However, in many real applications, such prior knowledge may not be available, or labeled information might be very limited. In this paper, we propose a new algorithm, called M
l -Forest , to learn an ensemble of hierarchical multi-label classifier trees to reveal the intrinsic label dependencies. In Ml -Forest , we construct a set of hierarchical trees, and develop a label transfer mechanism to identify the multiple relevant labels in a hierarchical way. In general, the relevant labels at higher levels of the trees capture more discriminable label concepts, and they will be transferred into lower level children nodes that are harder to discriminate. The relevant labels in the hierarchy are then aggregated to compute label dependency and make the final prediction. Our empirical study shows encouraging results of the proposed algorithm in comparison with the state-of-the-art multi-label classification algorithms under Friedman test and post-hoc Nemenyi test. [ABSTRACT FROM PUBLISHER]- Published
- 2016
- Full Text
- View/download PDF
42. Online Heterogeneous Transfer by Hedge Ensemble of Offline and Online Decisions.
- Author
-
Yan, Yuguang, Wu, Qingyao, Tan, Mingkui, Ng, Michael K., Min, Huaqing, and Tsang, Ivor W.
- Subjects
HETEROGENEOUS computing ,FEATURE extraction ,BIG data - Abstract
In this paper, we study the online heterogeneous transfer (OHT) learning problem, where the target data of interest arrive in an online manner, while the source data and auxiliary co-occurrence data are from offline sources and can be easily annotated. OHT is very challenging, since the feature spaces of the source and target domains are different. To address this, we propose a novel technique called OHT by hedge ensemble by exploiting both offline knowledge and online knowledge of different domains. To this end, we build an offline decision function based on a heterogeneous similarity that is constructed using labeled source data and unlabeled auxiliary co-occurrence data. After that, an online decision function is learned from the target data. Last, we employ a hedge weighting strategy to combine the offline and online decision functions to exploit knowledge from the source and target domains of different feature spaces. We also provide a theoretical analysis regarding the mistake bounds of the proposed approach. Comprehensive experiments on three real-world data sets demonstrate the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
43. MR-NTD: Manifold Regularization Nonnegative Tucker Decomposition for Tensor Data Dimension Reduction and Representation.
- Author
-
Li, Xutao, Ng, Michael K., Cong, Gao, Ye, Yunming, and Wu, Qingyao
- Subjects
- *
TENSOR algebra , *MATRIX decomposition - Abstract
With the advancement of data acquisition techniques, tensor (multidimensional data) objects are increasingly accumulated and generated, for example, multichannel electroencephalographies, multiview images, and videos. In these applications, the tensor objects are usually nonnegative, since the physical signals are recorded. As the dimensionality of tensor objects is often very high, a dimension reduction technique becomes an important research topic of tensor data. From the perspective of geometry, high-dimensional objects often reside in a low-dimensional submanifold of the ambient space. In this paper, we propose a new approach to perform the dimension reduction for nonnegative tensor objects. Our idea is to use nonnegative Tucker decomposition (NTD) to obtain a set of core tensors of smaller sizes by finding a common set of projection matrices for tensor objects. To preserve geometric information in tensor data, we employ a manifold regularization term for the core tensors constructed in the Tucker decomposition. An algorithm called manifold regularization NTD (MR-NTD) is developed to solve the common projection matrices and core tensors in an alternating least squares manner. The convergence of the proposed algorithm is shown, and the computational complexity of the proposed method scales linearly with respect to the number of tensor objects and the size of the tensor objects, respectively. These theoretical results show that the proposed algorithm can be efficient. Extensive experimental results have been provided to further demonstrate the effectiveness and efficiency of the proposed MR-NTD algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
44. Semi-supervised low-rank mapping learning for multi-label classification.
- Author
-
Liping Jing, Liu Yang, Jian Yu, and Ng, Michael K.
- Published
- 2015
- Full Text
- View/download PDF
45. Robust and Non-Negative Collective Matrix Factorization for Text-to-Image Transfer Learning.
- Author
-
Yang, Liu, Jing, Liping, and Ng, Michael K.
- Subjects
ROBUST control ,NONNEGATIVE matrices ,FACTORIZATION ,IMAGE processing ,MACHINE learning ,MATHEMATICAL domains - Abstract
Heterogeneous transfer learning has recently gained much attention as a new machine learning paradigm in which the knowledge can be transferred from source domains to target domains in different feature spaces. Existing works usually assume that source domains can provide accurate and useful knowledge to be transferred to target domains for learning. In practice, there may be noise appearing in given source (text) and target (image) domains data, and thus, the performance of transfer learning can be seriously degraded. In this paper, we propose a robust and non-negative collective matrix factorization model to handle noise in text-to-image transfer learning, and make a reliable bridge to transfer accurate and useful knowledge from the text domain to the image domain. The proposed matrix factorization model can be solved by an efficient iterative method, and the convergence of the iterative method can be shown. Extensive experiments on real data sets suggest that the proposed model is able to effectively perform transfer learning in noisy text and image domains, and it is superior to the popular existing methods for text-to-image transfer learning. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
46. Functional Module Analysis for Gene Coexpression Networks with Network Integration.
- Author
-
Zhang, Shuqin, Zhao, Hongyu, and Ng, Michael K.
- Abstract
Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
47. Learning Transferred Weights From Co-Occurrence Data for Heterogeneous Transfer Learning.
- Author
-
Yang, Liu, Jing, Liping, Yu, Jian, and Ng, Michael K.
- Subjects
TEXT mining ,CLASSIFICATION algorithms ,MARKOV chain Monte Carlo ,MULTIPLE correspondence analysis (Statistics) ,TRANSFER of training - Abstract
One of the main research problems in heterogeneous transfer learning is to determine whether a given source domain is effective in transferring knowledge to a target domain, and then to determine how much of the knowledge should be transferred from a source domain to a target domain. The main objective of this paper is to solve this problem by evaluating the relatedness among given domains through transferred weights. We propose a novel method to learn such transferred weights with the aid of co-occurrence data, which contain the same set of instances but in different feature spaces. Because instances with the same category should have similar features, our method is to compute their principal components in each feature space such that co-occurrence data can be rerepresented by these principal components. The principal component coefficients from different feature spaces for the same instance in the co-occurrence data have the same order of significance for describing the category information. By using these principal component coefficients, the Markov Chain Monte Carlo method is employed to construct a directed cyclic network where each node is a domain and each edge weight is the conditional dependence from one domain to another domain. Here, the edge weight of the network can be employed as the transferred weight from a source domain to a target domain. The weight values can be taken as a prior for setting parameters in the existing heterogeneous transfer learning methods to control the amount of knowledge transferred from a source domain to a target domain. The experimental results on synthetic and real-world data sets are reported to illustrate the effectiveness of the proposed method that can capture strong or weak relations among feature spaces, and enhance the learning performance of heterogeneous transfer learning. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
48. Alternating Direction Method of Multipliers for Nonlinear Image Restoration Problems.
- Author
-
Chen, Chuan, Ng, Michael K., and Zhao, Xi-Le
- Subjects
- *
MULTIPLIERS (Mathematical analysis) , *NONLINEAR theories , *IMAGE reconstruction , *PROBLEM solving , *IMAGE processing , *GAUSSIAN processes , *WHITE noise - Abstract
In this paper, we address the total variation (TV)-based nonlinear image restoration problems. In nonlinear image restoration problems, an original image is corrupted by a spatiallyinvariant blur, the build-in nonlinearity in imaging system, and the additive Gaussian white noise. We study the objective function consisting of the nonlinear least squares data-fitting term and the TV regularization term of the restored image. By making use of the structure of the objective function, an efficient alternating direction method of multipliers can be developed for solving the proposed model. The convergence of the numerical scheme is also studied. Numerical examples, including nonlinear image restoration and high-dynamic range imaging are reported to demonstrate the effectiveness of the proposed model and the efficiency of the proposed numerical scheme. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
49. Incremental Linear Discriminant Analysis: A Fast Algorithm and Comparisons.
- Author
-
Chu, Delin, Liao, Li-Zhi, Ng, Michael Kwok-Po, and Wang, Xiaoyan
- Subjects
DISCRIMINANT analysis ,ALGORITHM research ,INCREMENTAL motion control ,COMPUTATIONAL complexity ,CLASSIFICATION - Abstract
It has always been a challenging task to develop a fast and an efficient incremental linear discriminant analysis (ILDA) algorithm. For this purpose, we conduct a new study for linear discriminant analysis (LDA) in this paper and develop a new ILDA algorithm. We propose a new batch LDA algorithm called LDA/QR. LDA/QR is a simple and fast LDA algorithm, which is obtained by computing the economic QR factorization of the data matrix followed by solving a lower triangular linear system. The relationship between LDA/QR and uncorrelated LDA (ULDA) is also revealed. Based on LDA/QR, we develop a new incremental LDA algorithm called ILDA/QR. The main features of our ILDA/QR include that: 1) it can easily handle the update from one new sample or a chunk of new samples; 2) it has efficient computational complexity and space complexity; and 3) it is very fast and always achieves competitive classification accuracy compared with ULDA algorithm and existing ILDA algorithms. Numerical experiments based on some real-world data sets demonstrate that our ILDA/QR is very efficient and competitive with the state-of-the-art ILDA algorithms in terms of classification accuracy, computational complexity, and space complexity. [ABSTRACT FROM PUBLISHER]
- Published
- 2015
- Full Text
- View/download PDF
50. Patterned Fabric Inspection and Visualization by the Method of Image Decomposition.
- Author
-
Ng, Michael K., Ngan, Henry Y. T., Yuan, Xiaoming, and Zhang, Wenxing
- Subjects
- *
VISUALIZATION , *ALGORITHMS , *MATHEMATICAL optimization , *DECOMPOSITION method , *ACCURACY - Abstract
This paper analyzes the cartoon and texture structures to inspect and visualize defective objects in a patterned fabric image. It presents a method of an image decomposition (ID) and solves it by a convex optimization algorithm. Our experimental results on benchmark fabric images are superior to those by other methods. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
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