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1,488 results on '"NG, MICHAEL K."'

201. A Variational Method for Expanding the Bit-Depth of Low Contrast Image

Author

Qiao, Motong, Wang, Wei, Ng, Michael K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Heyden, Anders, editor, Kahl, Fredrik, editor, Olsson, Carl, editor, Oskarsson, Magnus, editor, and Tai, Xue-Cheng, editor

Published
2013
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213. L 0-Norm and Total Variation for Wavelet Inpainting

Author

Yau, Andy C., Tai, Xue-Cheng, Ng, Michael K., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Tai, Xue-Cheng, editor, Mørken, Knut, editor, Lysaker, Marius, editor, and Lie, Knut-Andreas, editor

Published
2009
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216. Adversarial Auto-encoder Domain Adaptation for Cold-start Recommendation with Positive and Negative Hypergraphs.

Author

HANRUI WU, JINYI LONG, NUOSI LI, DAHAI YU, and NG, MICHAEL K.

Subjects
HYPERGRAPHS, RECOMMENDER systems
Abstract

This article presents a novelmodel named Adversarial Auto-encoder Domain Adaptation to handle the recommendation problem under cold-start settings. Specifically, we divide the hypergraph into two hypergraphs, i.e., a positive hypergraph and a negative one. Below, we adopt the cold-start user recommendation for illustration. After achieving positive and negative hypergraphs, we apply hypergraph auto-encoders to them to obtain positive and negative embeddings of warm users and items. Additionally, we employ a multi-layer perceptron to get warm and cold-start user embeddings called regular embeddings. Subsequently, for warm users, we assign positive and negative pseudo-labels to their positive and negative embeddings, respectively, and treat their positive and regular embeddings as the source and target domain data, respectively. Then, we develop a matching discriminator to jointly minimize the classification loss of the positive and negative warm user embeddings and the distribution gap between the positive and regular warm user embeddings. In this way, warm users' positive and regular embeddings are connected. Since the positive hypergraph maintains the relations between positive warm user and item embeddings, and the regular warm and cold-start user embeddings follow a similar distribution, the regular cold-start user embedding and positive item embedding are bridged to discover their relationship. The proposed model can be easily extended to handle the coldstart item recommendation by changing inputs. We perform extensive experiments on real-world datasets for both cold-start user and cold-start item recommendations. Promising results in terms of precision, recall, normalized discounted cumulative gain, and hit rate verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2023
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217. A Control Model for Markovian Genetic Regulatory Networks

Author

Ng, Michael K., Zhang, Shu-Qin, Ching, Wai-Ki, Akutsu, Tatsuya, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Istrail, Sorin, editor, Pevzner, Pavel, editor, Waterman, Michael, editor, Priami, Corrado, editor, Hu, Xiaohua, editor, Pan, Yi, editor, and Lin, Tsau Young, editor

Published
2006
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218. Analysis of the ratio of ℓ1and ℓ2norms for signal recovery with partial support information

Author

Ge, Huanmin, Chen, Wengu, and Ng, Michael K

Abstract

The ratio of $\ell _{1}$and $\ell _{2}$norms, denoted as $\ell _{1}/\ell _{2}$, has presented prominent performance in promoting sparsity. By adding partial support information to the standard $\ell _{1}/\ell _{2}$minimization, in this paper, we introduce a novel model, i.e. the weighted $\ell _{1}/\ell _{2}$minimization, to recover sparse signals from the linear measurements. The restricted isometry property based conditions for sparse signal recovery in both noiseless and noisy cases through the weighted $\ell _{1}/\ell _{2}$minimization are established. And we show that the proposed conditions are weaker than the analogous conditions for standard $\ell _{1}/\ell _{2}$minimization when the accuracy of the partial support information is at least $50\%$. Moreover, we develop effective algorithms and illustrate our results via extensive numerical experiments on synthetic data in both noiseless and noisy cases.

Published
2023
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219. Morphological Feature Visualization of Alzheimer’s Disease via Multidirectional Perception GAN

Author

Yu, Wen, Lei, Baiying, Wang, Shuqiang, Liu, Yong, Feng, Zhiguang, Hu, Yong, Shen, Yanyan, and Ng, Michael K.

Abstract

The diagnosis of early stages of Alzheimer’s disease (AD) is essential for timely treatment to slow further deterioration. Visualizing the morphological features for early stages of AD is of great clinical value. In this work, a novel multidirectional perception generative adversarial network (MP-GAN) is proposed to visualize the morphological features indicating the severity of AD for patients of different stages. Specifically, by introducing a novel multidirectional mapping mechanism into the model, the proposed MP-GAN can capture the salient global features efficiently. Thus, using the class discriminative map from the generator, the proposed model can clearly delineate the subtle lesions via MR image transformations between the source domain and the predefined target domain. Besides, by integrating the adversarial loss, classification loss, cycle consistency loss, and ${L}1$ penalty, a single generator in MP-GAN can learn the class discriminative maps for multiple classes. Extensive experimental results on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that MP-GAN achieves superior performance compared with the existing methods. The lesions visualized by MP-GAN are also consistent with what clinicians observe.

Published
2023
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220. FastHyMix: Fast and Parameter-Free Hyperspectral Image Mixed Noise Removal

Author

Zhuang, Lina and Ng, Michael K.

Abstract

The decrease in the widths of spectral bands in hyperspectral imaging leads to a decrease in signal-to-noise ratio (SNR) of measurements. The decreased SNR reduces the reliability of measured features or information extracted from hyperspectral images (HSIs). Furthermore, the image degradations linked with various mechanisms also result in different types of noise, such as Gaussian noise, impulse noise, deadlines, and stripes. This article introduces a fast and parameter-free hyperspectral image mixed noise removal method (termed FastHyMix), which characterizes the complex distribution of mixed noise by using a Gaussian mixture model and exploits two main characteristics of hyperspectral data, namely, low rankness in the spectral domain and high correlation in the spatial domain. The Gaussian mixture model enables us to make a good estimation of Gaussian noise intensity and the locations of sparse noise. The proposed method takes advantage of the low rankness using subspace representation and the spatial correlation of HSIs by adding a powerful deep image prior, which is extracted from a neural denoising network. An exhaustive array of experiments and comparisons with state-of-the-art denoisers was carried out. The experimental results show significant improvement in both synthetic and real datasets. A MATLAB demo of this work is available at https://github.com/LinaZhuang for the sake of reproducibility.

Published
2023
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221. Fast Algorithms for l1 Norm/Mixed l1 and l2 Norms for Image Restoration

Author

Fu, Haoying, Ng, Michael K., Nikolova, Mila, Barlow, Jesse, Ching, Wai-ki, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Gervasi, Osvaldo, editor, Gavrilova, Marina L., editor, Kumar, Vipin, editor, Laganá, Antonio, editor, Lee, Heow Pueh, editor, Mun, Youngsong, editor, Taniar, David, editor, and Tan, Chih Jeng Kenneth, editor

Published
2005
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222. On the Performance of Feature Weighting K-Means for Text Subspace Clustering

Author

Jing, Liping, Ng, Michael K., Xu, Jun, Huang, Joshua Zhexue, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Fan, Wenfei, editor, Wu, Zhaohui, editor, and Yang, Jun, editor

Published
2005
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223. Subspace Clustering of Text Documents with Feature Weighting K-Means Algorithm

Author

Jing, Liping, Ng, Michael K., Xu, Jun, Huang, Joshua Zhexue, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Ho, Tu Bao, editor, Cheung, David, editor, and Liu, Huan, editor

Published
2005
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224. Discretization of Multidimensional Web Data for Informative Dense Regions Discovery

Author

Wu, Edmond H., Ng, Michael K., Yip, Andy M., Chan, Tony F., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Zhang, Jun, editor, He, Ji-Huan, editor, and Fu, Yuxi, editor

Published
2005
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230. A Clustering Model for Mining Evolving Web User Patterns in Data Stream Environment

Author

Wu, Edmond H., Ng, Michael K., Yip, Andy M., Chan, Tony F., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Zheng Rong, editor, Yin, Hujun, editor, and Everson, Richard M., editor

Published
2004
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231. Building Genetic Networks for Gene Expression Patterns

Author

Ching, Wai-Ki, Fung, Eric S., Ng, Michael K., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Zheng Rong, editor, Yin, Hujun, editor, and Everson, Richard M., editor

Published
2004
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232. Unsupervised Dense Regions Discovery in DNA Microarray Data

Author

Yip, Andy M., Wu, Edmond H., Ng, Michael K., Chan, Tony F., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Yang, Zheng Rong, editor, Yin, Hujun, editor, and Everson, Richard M., editor

Published
2004
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233. A Feature Weighting Approach to Building Classification Models by Interactive Clustering

Author

Jing, Liping, Huang, Joshua, Ng, Michael K., Rong, Hongqiang, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Torra, Vicenç, editor, and Narukawa, Yasuo, editor

Published
2004
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234. An Efficient Algorithm for Dense Regions Discovery from Large-Scale Data Streams

Author

Yip, Andy M., Wu, Edmond H., Ng, Michael K., Chan, Tony F., Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Dai, Honghua, editor, Srikant, Ramakrishnan, editor, and Zhang, Chengqi, editor

Published
2004
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235. Mining of Web-Page Visiting Patterns with Continuous-Time Markov Models

Author

Huang, Qiming, Yang, Qiang, Huang, Joshua Zhexue, Ng, Michael K., Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Dai, Honghua, editor, Srikant, Ramakrishnan, editor, and Zhang, Chengqi, editor

Published
2004
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236. An Efficient Multidimensional Data Model for Web Usage Mining

Author

Wu, Edmond H., Ng, Michael K., Huang, Joshua Z., Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Yu, Jeffrey Xu, editor, Lin, Xuemin, editor, Lu, Hongjun, editor, and Zhang, Yanchun, editor

Published
2004
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241. Patterns Discovery Based on Time-Series Decomposition

Author

Yu, Jeffrey Xu, Ng, Michael K., Huang, Joshua Zhexue, Goos, G., editor, Hartmanis, J., editor, van Leeuwen, J., editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Cheung, David, editor, Williams, Graham J., editor, and Li, Qing, editor

Published
2001
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247. Building Higher-Order Markov Chain Models with EXCEL

Author

Ching, Wai-Ki, Fung, Eric S., and Ng, Michael K.

Abstract

Categorical data sequences occur in many applications such as forecasting, data mining and bioinformatics. In this note, we present higher-order Markov chain models for modelling categorical data sequences with an efficient algorithm for solving the model parameters. The algorithm can be implemented easily in a Microsoft EXCEL worksheet. We give a detailed description for the implementation which is accessible and useful to anyone who is interested in the applications of higher-order Markov chain models and has some knowledge of EXCEL.

Published
2004

248. Building Simple Hidden Markov Models. Classroom Notes

Author

Ching, Wai-Ki and Ng, Michael K.

Abstract

Hidden Markov models (HMMs) are widely used in bioinformatics, speech recognition and many other areas. This note presents HMMs via the framework of classical Markov chain models. A simple example is given to illustrate the model. An estimation method for the transition probabilities of the hidden states is also discussed.

Published
2004

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