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Finding a consensus embedding from multiple views is the mainstream task in multiview graph-based clustering, in which the key problem is to handle the inconsistence among multiple views. In this article, we consider clustering effectiveness and practical applicability collectively, and propose a parameter-free model to alleviate the inconsistence of multiple views cleverly. To be specific, the proposed model considers the diversities of multiple views as two-layers. The first layer considers the inconsistence among different features of each view and the second layer considers linking the preembeddings of multiple views attentively. By this way, a consensus embedding can be learned via kernel method effectively and the whole learning procedure is parameter-free. To solve the optimization problem involved in the proposed model, we propose an alternative algorithm which is efficient and easy to implement in practice. In the experiments, we evaluate the proposed model on synthetic and real datasets and the experimental results demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Remote sensing image scene classification has attracted great attention because of its wide applications. Although convolutional neural network (CNN)-based methods for scene classification have achieved excellent results, the large-scale variation of the features and objects in remote sensing images limits the further improvement of the classification performance. To address this issue, we present multiscale representation for scene classification, which is realized by a global–local two-stream architecture. This architecture has two branches of the global stream and local stream, which can individually extract the global features and local features from the whole image and the most important area. In order to locate the most important area in the whole image using only image-level labels, a weakly supervised key area detection strategy of structured key area localization (SKAL) is specially designed to connect the above two streams. To verify the effectiveness of the proposed SKAL-based two-stream architecture, we conduct comparative experiments based on three widely used CNN models, including AlexNet, GoogleNet, and ResNet18, on four public remote sensing image scene classification data sets, and achieve the state-of-the-art results on all the four data sets. Our codes are provided in https://github.com/hw2hwei/SKAL. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) and spectral clustering are two of the most widely used clustering techniques. However, NMF cannot deal with the nonlinear data, and spectral clustering relies on the postprocessing. In this article, we propose a Robust Matrix factorization with Spectral embedding (RMS) approach for data clustering, which inherits the advantages of NMF and spectral clustering, while avoiding their shortcomings. In addition, to cluster the data represented by multiple views, we present the multiview version of RMS (M-RMS), and the weights of different views are self-tuned. The main contributions of this research are threefold: 1) by integrating spectral clustering and matrix factorization, the proposed methods are able to capture the nonlinear data structure and obtain the cluster indicator directly; 2) instead of using the squared Frobenius-norm, the objectives are developed with the $\ell _{2,1}$ -norm, such that the effects of the outliers are alleviated; and 3) the proposed methods are totally parameter-free, which increases the applicability for various real-world problems. Extensive experiments on several single-view/multiview data sets demonstrate the effectiveness of our methods and verify their superior clustering performance over the state of the arts. [ABSTRACT FROM AUTHOR]

Data clustering is a fundamental problem in the field of machine learning. Among the numerous clustering techniques, matrix factorization-based methods have achieved impressive performances because they are able to provide a compact and interpretable representation of the input data. However, most of the existing works assume that each class has a global centroid, which does not hold for data with complicated structures. Besides, they cannot guarantee that the sample is associated with the nearest centroid. In this work, we present a concept factorization with the local centroids (CFLCs) approach for data clustering. The proposed model has the following advantages: 1) the samples from the same class are allowed to connect with multiple local centroids such that the manifold structure is captured; 2) the pairwise relationship between the samples and centroids is modeled to produce a reasonable label assignment; and 3) the clustering problem is formulated as a bipartite graph partitioning task, and an efficient algorithm is designed for optimization. Experiments on several data sets validate the effectiveness of the CFLC model and demonstrate its superior performance over the state of the arts. [ABSTRACT FROM AUTHOR]

Video summarization is the technique to condense large-scale videos into summaries composed of key-frames or key-shots so that the viewers can browse the video content efficiently. Recently, supervised approaches have achieved great success by taking advantages of recurrent neural networks (RNNs). Most of them focus on generating summaries by maximizing the overlap between the generated summary and the ground truth. However, they neglect the most critical principle, i.e., whether the viewer can infer the original video content from the summary. As a result, existing approaches cannot preserve the summary quality well and usually demand large amounts of training data to reduce overfitting. In our view, video summarization has two tasks, i.e., generating summaries from videos and inferring the original content from summaries. Motivated by this, we propose a dual learning framework by integrating the summary generation (primal task) and video reconstruction (dual task) together, which targets to reward the summary generator under the assistance of the video reconstructor. Moreover, to provide more guidance to the summary generator, two property models are developed to measure the representativeness and diversity of the generated summary. Practically, experiments on four popular data sets (SumMe, TVsum, OVP, and YouTube) have demonstrated that our approach, with compact RNNs as the summary generator, using less training data, and even in the unsupervised setting, can get comparable performance with those supervised ones adopting more complex summary generators and trained on more annotated data. [ABSTRACT FROM AUTHOR]

Feature representation learning, an emerging topic in recent years, has achieved great progress. Powerful learned features can lead to excellent classification accuracy. In this article, a selective and robust feature representation framework with a supervised constraint (SRSC) is presented. SRSC seeks a selective, robust, and discriminative subspace by transforming the original feature space into the category space. Particularly, we add a selective constraint to the transformation matrix (or classifier parameter) that can select discriminative dimensions of the input samples. Moreover, a supervised regularization is tailored to further enhance the discriminability of the subspace. To relax the hard zero-one label matrix in the category space, an additional error term is also incorporated into the framework, which can lead to a more robust transformation matrix. SRSC is formulated as a constrained least square learning (feature transforming) problem. For the SRSC problem, an inexact augmented Lagrange multiplier method (ALM) is utilized to solve it. Extensive experiments on several benchmark data sets adequately demonstrate the effectiveness and superiority of the proposed method. The proposed SRSC approach has achieved better performances than the compared counterpart methods. [ABSTRACT FROM AUTHOR]

For large-scale image retrieval task, a hashing technique has attracted extensive attention due to its efficient computing and applying. By using the hashing technique in image retrieval, it is crucial to generate discrete hash codes and preserve the neighborhood ranking information simultaneously. However, both related steps are treated independently in most of the existing deep hashing methods, which lead to the loss of key category-level information in the discretization process and the decrease in discriminative ranking relationship. In order to generate discrete hash codes with notable discriminative information, we integrate the discretization process and the ranking process into one architecture. Motivated by this idea, a novel ranking optimization discrete hashing (RODH) method is proposed, which directly generates discrete hash codes (e.g., +1/−1) from raw images by balancing the effective category-level information of discretization and the discrimination of ranking information. The proposed method integrates convolutional neural network, discrete hash function learning, and ranking function optimizing into a unified framework. Meanwhile, a novel loss function based on label information and mean average precision (MAP) is proposed to preserve the label consistency and optimize the ranking information of hash codes simultaneously. Experimental results on four benchmark data sets demonstrate that RODH can achieve superior performance over the state-of-the-art hashing methods. [ABSTRACT FROM AUTHOR]

Unsupervised feature selection is fundamentally important for processing unlabeled high-dimensional data, and several methods have been proposed on this topic. Most existing embedded unsupervised methods just emphasize the data structure in the input space, which may contain large noise. Therefore, they are limited to perceive the discriminative information implied within the low-dimensional manifold. In addition, these methods always involve several parameters to be tuned, which is time-consuming. In this paper, we present a self-tuned discrimination-aware (STDA) approach for unsupervised feature selection. The main contributions of this paper are threefold: 1) it adopts the advantage of discriminant analysis technique to select the valuable features; 2) it learns the local data structure adaptively in the discriminative subspace to alleviate the effect of data noise; and 3) it performs feature selection and clustering simultaneously with an efficient optimization strategy, and saves the additional efforts to tune parameters. Experimental results on a toy data set and various real-world benchmarks justify the effectiveness of STDA on both feature selection and data clustering, and demonstrate its promising performance against the state of the arts. [ABSTRACT FROM AUTHOR]

Random projection is a popular machine learning algorithm, which can be implemented by neural networks and trained in a very efficient manner. However, the number of features should be large enough when applied to a rather large-scale data set, which results in slow speed in testing procedure and more storage space under some circumstances. Furthermore, some of the features are redundant and even noisy since they are randomly generated, so the performance may be affected by these features. To remedy these problems, an effective feature selection method is introduced to select useful features hierarchically. Specifically, a novel criterion is proposed to select useful neurons for neural networks, which establishes a new way for network architecture design. The testing time and accuracy of the proposed method are improved compared with traditional methods and some variations on both classification and regression tasks. Extensive experiments confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Feature extraction plays a significant role in pattern recognition. Recently, many representation-based feature extraction methods have been proposed and achieved successes in many applications. As an excellent unsupervised feature extraction method, latent low-rank representation (LatLRR) has shown its power in extracting salient features. However, LatLRR has the following three disadvantages: 1) the dimension of features obtained using LatLRR cannot be reduced, which is not preferred in feature extraction; 2) two low-rank matrices are separately learned so that the overall optimality may not be guaranteed; and 3) LatLRR is an unsupervised method, which by far has not been extended to the supervised scenario. To this end, in this paper, we first propose to use two different matrices to approximate the low-rank projection in LatLRR so that the dimension of obtained features can be reduced, which is more flexible than original LatLRR. Then, we treat the two low-rank matrices in LatLRR as a whole in the process of learning. In this way, they can be boosted mutually so that the obtained projection can extract more discriminative features. Finally, we extend LatLRR to the supervised scenario by integrating feature extraction with the ridge regression. Thus, the process of feature extraction is closely related to the classification so that the extracted features are discriminative. Extensive experiments are conducted on different databases for unsupervised and supervised feature extraction, and very encouraging results are achieved in comparison with many state-of-the-arts methods. [ABSTRACT FROM AUTHOR]