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Discriminative Multiview nonnegative matrix factorization for Classification

IEEE ACCESS 2019年 7卷 60947-60956页

作者： Ou, Weihua Gou, Jianping Zhou, Quan Ge, Shiming Long, Fei Guizhou Normal Univ Sch Big Data & Comp Sci Guiyang 550001 Guizhou Peoples R China Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Natl Engn Res Ctr Commun & Networking Nanjing 210023 Jiangsu Peoples R China Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China Guizhou Inst Technol Sch Elect & Informat Engn Guiyang 550003 Guizhou Peoples R China

Multiview nonnegative matrix has shown many promising applications in computer vision and pattern recognition. However, most existing works focus on view consistency and ignore discrimination. In this paper, we introduce a novel discriminative multiview nonnegative matrix (DMultiNMF) algorithm to learn discriminative and consistent representations for facilitating classification. In this algorithm, we apply discriminative patch alignment to enhance the local discrimination in each view and utilize the large margin principle to improve global discrimination. At the same time, we use a shared representation to propagate information among the multiple views to ensure consistency. Apart from that, we measure the reconstruction errors utilizing the correntropy-induced metric to improve the robustness. The experiments on face recognition, handwritten digit recognition, Xmedia, and Wikipedia multiview data sets demonstrate the advantages of the proposed method compared with other algorithms like single view using concatenated views and substantially better than other multiview nonnegative matrix factorization algorithms.

关键词： Multiview learning nonnegative matrix factorization patch alignment consistent representation classification

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nonnegative matrix factorization with manifold structure for face recognition

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INTERNATIONAL JOURNAL OF WAVELETS MULTIRESOLUTION AND INFORMATION PROCESSING 2019年第2期17卷

作者： Chen, Wen-Sheng Wang, Chian Pan, Binbin Chen, Bo Shenzhen Univ Coll Math & Stat Shenzhen 518060 Peoples R China Guangdong Key Lab Intelligent Informat Proc Shenzhen 518060 Peoples R China

nonnegative matrix factorization (NMF) is a promising method to represent facial images using nonnegative features under a low-rank nonnegative basis-image matrix. The facial images usually reside on a low-dimensional manifold due to the variations of illumination, pose and facial expression. However, NMF has no ability to uncover the manifold structure of data embedded in a high-dimensional Euclidean space, while the manifold structure contains both local and nonlocal intrinsic features. These two kinds of features are of benefit to class discrimination. To enhance the discriminative power of NMF, this paper proposes a novel NMF algorithm with manifold structure (Mani-NMF). Two quantities related to adjacent graph and non-adjacent graph are incorporated into the objective function, which will be minimized by solving two convex suboptimization problems. Based on the gradient descent method and auxiliary function technique, we acquire the update rules of Mani-NMF and theoretically prove the convergence of the proposed Mani-NMF algorithm. Three publicly available face databases, Yale, pain expression and CMU databases, are selected for evaluations. Experiments results show that our algorithm achieves a better performance than some state-of-the-art algorithms.

关键词： nonnegative matrix factorization face recognition multi-manifold learning

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Two fast vector-wise update algorithms for orthogonal nonnegative matrix factorization with sparsity constraint

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2020年 375卷

作者： Li, Wenbo Li, Jicheng Liu, Xuenian Dong, Liqiang Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China

Recently, orthogonal nonnegative matrix factorization (ONMF) has been introduced and shown to work remarkably well for clustering tasks. Because of the nonnegativity and the orthogonality constraints, the orthogonal factor matrix of ONMF is naturally sparse. Based on this fact, by introducing sparsity constraints on the orthogonal coefficient matrix, we propose two vector-wise algorithms based on Hierarchical Alternating Least Squares (HALS) and Block Prox-linear (BPL) methods to the approximately sparse orthogonal nonnegative matrix factorization (SONMF). Some global convergence results are established under the mild conditions. Numerical results including synthetic and real-world datasets are given to illustrate that the proposed algorithms compute highly accurate values and perform better than the other testing ONMF methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： nonnegative matrix factorization Orthogonality constraint Sparsity constraint Alternating least squares method Clustering method

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Cograph Regularized Collective nonnegative matrix factorization for Multilabel Image Annotation

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IEEE ACCESS 2019年 7卷 88338-88356页

作者： Zhang, Juli He, Zhanzhuang Zhang, Junyi Dai, Tao Xian Microelect Technol Inst Dept Res & Dev Xian 710068 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Shaanxi Peoples R China China Minsheng Bank Beijing 110600 Peoples R China

Automatic image annotation is an effective and straightforward way to facilitate many applications in computer vision. However, manually annotating images is a computation-expensive and labor-intensive task. To address these problems, this paper proposes a novel approach by using a cograph regularized collective nonnegative matrix factorization method to annotate images, which is referred to as CG-CNMF;CG-CNMF maximizes the annotation consistency for each image and minimizes the semantic gap for good annotation performance. To reduce the computation cost, this method formulates the annotation problem as a recommending issue and uses nonnegative matrix factorization (NMF) to recover the image-to label relation for the testing images. Moreover, to find the most similar latent image features and latent label features during the matrix factorization, it exploits the image-to-image relation and label-to-label relation by utilizing the visual content information of images and the semantic cooccurrence information of labels, respectively. To reduce the semantic gap between the image visual content and semantic concepts, both the semantic features and convolutional neural networks (CNNs)-based visual features are considered. Moreover, to address the label-imbalance and incomplete-label problems, the visual-based label cooccurrence information is also considered. In this way, visually similar images are highly correlated with the true semantics of the test images. The experimental results for three multilabel image datasets demonstrate the effectiveness and the efficiency of the proposed method.

关键词： Image annotation nonnegative matrix factorization collective nonnegative matrix factorization semantic gap convolutional neural networks

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Methods for nonnegative matrix factorization Based on Low-Rank Cross Approximations

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COMPUTATIONAL MATHEMATICS AND MATHEMATICAL PHYSICS 2019年第8期59卷 1251-1266页

作者： Tyrtyshnikov, E. E. Shcherbakova, E. M. Russian Acad Sci Inst Numer Math Moscow 119333 Russia Moscow MV Lomonosov State Univ Fac Computat Math & Cybernet Moscow 119991 Russia

Available methods for nonnegative matrix factorization make use of all elements of the original matrix, and their complexity is at least which makes them extremely resource-intensive in the case of large amounts of data. Accordingly, the following natural question arises: given the nonnegative rank of a matrix, can a nonnegative matrix factorization be constructed using some of its rows and columns? Methods for solving this problem are proposed for certain classes of matrices, namely, for nonnegative separable matrices (for which there exists a cone spanned by several columns of the original matrix that contains all its columns), for nonnegative separable matrices with perturbations, and for nonnegative matrices of rank 2. In practice, the number of operations and the amount of storage used by the proposed algorithms depend linearly on m + n.

关键词： nonnegative matrix factorization separable matrices fast algorithms cross approximation methods

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Community Detection in Multi-Layer Networks Using Joint nonnegative matrix factorization

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2019年第2期31卷 273-286页

作者： Ma, Xiaoke Dong, Di Wang, Quan Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China Chinese Acad Sci Inst Automat Key Lab Mol Imaging Beijing 100190 Peoples R China

Many complex systems are composed of coupled networks through different layers, where each layer represents one of many possible types of interactions. A fundamental question is how to extract communities in multi-layer networks. The current algorithms either collapses multi-layer networks into a single-layer network or extends the algorithms for single-layer networks by using consensus clustering. However, these approaches have been criticized for ignoring the connection among various layers, thereby resulting in low accuracy. To attack this problem, a quantitative function (multi-layer modularity density) is proposed for community detection in multi-layer networks. Afterward, we prove that the trace optimization of multi-layer modularity density is equivalent to the objective functions of algorithms, such as kernel K-means, nonnegative matrix factorization (NMF), spectral clustering and multi-view clustering, for multi-layer networks, which serves as the theoretical foundation for designing algorithms for community detection. Furthermore, a Semi-Supervised joint nonnegative matrix factorization algorithm (S2-jNMF) is developed by simultaneously factorizing matrices that are associated with multi-layer networks. Unlike the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the S2-jNMF algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method outperforms the state-of-the-art approaches for community detection in multi-layer networks.

关键词： Multi-layer networks community structure nonnegative matrix factorization semi-supervised clustering

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Large Basic Cone and Sparse Subspace Constrained nonnegative matrix factorization With Kullback-Leibler Divergence for Data Representation

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IEEE INTELLIGENT SYSTEMS 2019年第4期34卷 39-47页

作者： Viet-Hang Duong Manh-Quan Bui Li, Yung-Hui Tai, Tzu-Chiang Wang, Jia-Ching Bac Lieu Univ Bac Lieu Vietnam Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan Taiwan Providence Univ Dept Comp Sci & Informat Engn Shalu Taiwan Pervas Artificial Intelligence Res PAIR Labs Taipei Taiwan

In this article, a new constrained NMF model with Kullback-Leibler (KL) divergence is developed for data representation. It is called large basic cone and sparse representation-constrained nonnegative matrix factorization with Kullback-Leibler divergence (conespaNMF_KL). It achieves sparseness from a large simplicial cone constraint on the base and sparse regularize on the extracted features.

关键词： Data Structures matrix Decomposition Basic Cone Kullback Leibler Divergence Data Representation Constrained NMF Model Sparse Subspace Constrained nonnegative matrix factorization Sparse Matrices Face Recognition Intelligent Systems Task Analysis Principal Component Analysis Image Recognition Encoding Feature Extraction Data Models Data Representation nonnegative matrix factorization Projected Gradient Descent Face Recognition Facial Expression Recognition

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Entire frame image display employing monotonic convergent nonnegative matrix factorization

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2019年第6期16卷 2189-2211页

作者： Soniwal, Yogesh Mitra, Amit Venkatesh, K. S. Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India OVID Capital LLC Dept Res & Dev Bangalore Karnataka India Indian Inst Technol Kanpur Dept Math & Stat Kanpur Uttar Pradesh India

The average life spans of light-emitting diodes (LEDs) used in organic light-emitting diode displays are negatively influenced by high current operation in short duty cycles. Since the time average brightness of a pixel LED is a function only of the time integral of the delivered current, it is desirable to achieve brightness by adopting pixel addressing schemes that employ long duty cycles. Conventional addressing methods which cannot operate more than one row of pixels at a time inherently have very short duty cycles. We solve the problem by proposing entire frame addressing, made possible by a new image factorization called monotonic-nonnegative matrix factorization (M-NNMF). Using M-NNMF, we develop a converging image series representation of an arbitrary nonnegative matrix (image). Each element of the series has unit rank, which allows it to drive an entire frame simultaneously. Each application of the M-NNMF algorithm produces a dominant unit rank component for the series, and a residue image of higher rank, the input to the next iteration. We drive the display with these unit rank components, called sub-frames, each for a duration proportional to its energy. The sum of sub-frames approximates the original image and provides the same visual effect, due to inherent perceptual integration. M-NNMF, more efficient and less time complex compared to existing NNMF algorithms, is our primary contribution in this paper and is likely to find applications in many other situations. We also obtain an even faster converging series with a randomized version of M-NNMF. The proposed approach of entire frame image display is demonstrated on a wide variety of images.

关键词： Pixel addressing Diode life span nonnegative matrix factorization Entire frame image display

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Vehicle Face Recognition Algorithm Based on Weighted nonnegative matrix factorization with Double Regularization Terms

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2020年第5期14卷 2171-2185页

作者： Shi, Chunhe Wu, Chengdong Northeastern Univ Coll Informat Sci & Engn Shenyang Liaoning Peoples R China Northeastern Univ Fac Robot Sci & Engn Shenyang Liaoning Peoples R China

In order to judge that whether the vehicles in different images which are captured by surveillance cameras represent the same vehicle or not, we proposed a novel vehicle face recognition algorithm based on improved nonnegative matrix factorization (NMF), different from traditional vehicle recognition algorithms, there are fewer effective features in vehicle face image than in whole vehicle image in general, which brings certain difficulty to recognition. The innovations mainly include the following two aspects: 1) we proposed a novel idea that the vehicle type can be determined by a few key regions of the vehicle face such as logo, grille and so on;2) Through adding weight, sparseness and classification property constraints to the NMF model, we can acquire the effective feature bases that represent the key regions of vehicle face image. Experimental results show that the proposed algorithm not only achieve a high correct recognition rate, but also has a strong robustness to some non-cooperative factors such as illumination variation.

关键词： Vehicle face recognition vehicle matching nonnegative matrix factorization classification property gradient descent method

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Separation of multiple local-damage-related components from vibration data using nonnegative matrix factorization and multichannel data fusion

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2020年 145卷

作者： Wodecki, Jacek Michalak, Anna Zimroz, Radoslaw Wylomanska, Agnieszka Wroclaw Univ Sci & Technol Fac Geoengn Min & Geol Na Grobli 15 PL-50421 Wroclaw Poland Wroclaw Univ Sci & Technol Fac Pure & Appl Math Hugo Steinhaus Ctr Wybrze & Wyspianskiego 27 PL-50370 Wroclaw Poland

The problem of local damage detection in case of analyzing vibration signal from rotating machines is mostly related to the detection of periodic impulsive components. Depending on various features of the signal, this task can be relatively simple in some cases (e.g. for an impulsive component in the presence of Gaussian noise). However, in our case multiple impulsive components occupying with the overlapping frequency bands are present. For all components, the impulses can be periodic. In this article, authors present a novel methodology based on data fusion from multichannel vibration data from heavy-duty industrial gearbox operating in the driving station of a belt conveyor. The proposed method is based on the factorization of spectrograms using Generalized Hierarchical Alternating Least Squares nonnegative matrix factorization with Beta-Divergence (later referred to as beta-HALS NMF). Partial information obtained from the factorization is fused into a single data set for each impulsive component present in the signal. Finally, Griffin-Lim algorithm is used to estimate the complex phase layer of artificial spectrograms allowing to recover the near-perfect time series of each impulsive component extracted from the signal. This method has been tested on four-channel vibration data. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： nonnegative matrix factorization Beta-divergence Local damage detection Time-frequency representation Phase reconstruction Data fusion Vibration

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