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nonnegative matrix factorization based on complex generative model

ACOUSTICAL SCIENCE AND TECHNOLOGY 2019年第3期40卷 155-161页

作者： Kitamura, Daichi Kagawa Coll Natl Inst Technol Dept Elect & Comp Engn Chokushi 355 Takamatsu Kagawa 7618058 Japan

nonnegative matrix factorization (NMF) is a powerful technique of extracting meaningful patterns from an observed matrix and has been used for many applications in the audio signal processing field. In this article, the principle of NMF and some extensions based on a complex generative model are reviewed. Also, their application to audio source separation is presented.

关键词： nonnegative matrix factorization Complex generative model Audio source separation

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nonnegative matrix factorization-based blind source separation for full-field and high-resolution modal identification from video

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JOURNAL OF SOUND AND VIBRATION 2020年 487卷 115586-115586页

作者： Silva, Moises Martinez, Bridget Figueiredo, Eloi Costa, Joao C. W. A. Yang, Yongchao Mascarenas, David Fed Univ Para Appl Electromagnetism Lab Guama 01 BR-66075110 Belem Para Brazil Los Alamos Natl Lab Engn Inst POB 1663MS T001 Los Alamos NM 87545 USA Lusofona Univ Fac Engn Campo Grande 376 P-1749024 Lisbon Portugal Inst R&D Struct & Construct CONSTRUCT R Dr Roberto Frias S-N P-4200465 Porto Portugal Michigan Technol Univ Dept Mech Engn Houghton MI 49931 USA

Traditional modal analysis requires physically attached sensors for data acquisition and vibration-based monitoring. Although traditional modal analysis presents well-established techniques for dynamics analysis, they can impose mass-loading effects on lightweight structures and increase budgetary demands on the maintenance of such data acquisition systems. Recently video-based techniques have become of increasing interest in the identification of the dynamic properties of infrastructures with arbitrary complexity. However, most applications rely on frame by frame tracking of fixed speckle targets to derive time-varying physical parameters. This imposes serious limitations for real-world applications, especially in scenarios where the structure is out of reach. Therefore, to address these issues, we propose a novel output-only operational modal analysis method based on visionbased blind source separation scheme. The proposed algorithm makes use of each pixel as a potential measurement point. This enables an increase in the spatial density of sensors conventionally used on a structure by orders of magnitude. This simultaneous processing of all pixel time-series derives full-field high-resolution mode shapes instead of low spatial resolution mode shapes achieved when measuring a limited number of discrete locations with typical sensors. Compared to other approaches, we propose a blind source separation scheme simpler than the ones based on phase extraction and complex steerable pyramids that still capable of disentangling local structural vibration from video measurement only. Moreover, a simple method to magnify and visualize independent vibration modes is introduced using the extracted modal information only. We validate our method by laboratory experiments on a bench-scale building structure and a cantilever beam. The results demonstrate that the proposed technique can decompose high-resolution modal parameters, visualize and reconstruct even those weakly-excited v

关键词： Video-based dynamics monitoring Non-contact measurements Modal disentangling Blind source separation Output-only modal analysis nonnegative matrix factorization

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Novel Proximal Gradient Methods for nonnegative matrix factorization with Sparsity Constraints

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SIAM JOURNAL ON IMAGING SCIENCES 2020年第1期13卷 381-421页

作者： Teboulle, Marc Vaisbourd, Yakov Tel Aviv Univ Sch Math Sci IL-69978 Ramat Aviv Israel

We consider the nonnegative matrix factorization (NMF) problem with sparsity constraints formulated as a nonconvex composite minimization problem. We introduce four novel proximal gradient based algorithms proven globally convergent to a critical point and which are applicable to sparsity constrained NMF models. Our approach builds on recent results allowing one to lift the classical global Lipschitz continuity requirement through the use of a non-Euclidean Bregman based distance. Since under the proposed framework we are not restricted by the gradient Lipschitz continuity assumption, we can consider new decomposition settings of the NMF problem. Two of the derived schemes are genuine non-Euclidean proximal methods that tackle nonstandard decompositions of the NMF problem. The two other schemes are novel extensions of the well-known state-of-the-art methods (the multiplicative and hierarchical alternating least squares), thus allowing one to significantly broaden the scope of these algorithms. Numerical experiments illustrate the performance of the proposed methods.

关键词： nonnegative matrix factorization sparsity constraints nonconvex nonsmooth composite minimization proximal gradient algorithms non-Euclidean Bregman distance Kurdyka-Losiajewicz property essentially cyclic block proximal gradient global convergence

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Co-regularized nonnegative matrix factorization for evolving community detection in dynamic networks

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INFORMATION SCIENCES 2020年 528卷 265-279页

作者： Ma, Xiaoke Zhang, Benhui Ma, Changzhou Ma, Zhiyu Xidian Univ Sch Comp Sci & Technol 2 South Taibai Rd Xian Shaanxi Peoples R China

Community detection in static networks solely emphasizes the clustering accuracy, while evolving community detection in dynamic networks simultaneously takes into account both the clustering accuracy and clustering drift. The available evolutionary clustering algorithms are criticized for failing to fully characterize dynamics of networks and to accurately balance the clustering accuracy and clustering drift. To solve these problems, we propose a co-regularized evolutionary nonnegative matrix factorization for evolving communities in dynamic networks (Cr-ENMF). Specifically, both the network and communities at the previous time step are utilized to characterize the clustering drift, which are incorporated into the objective function of Cr-ENMF by regularization. We show that the well-known temporal smoothness framework for evolutionary clustering is a special case of the proposed framework, and prove the equivalence between Cr-ENMF and evolutionary clustering. Thereafter, an iterative strategy is presented to optimize the objective function. The experimental results over both artificial and real world dynamic networks illustrate that Cr-ENMF outperforms state-of-the-art approaches. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Evolving community Dynamic networks Graph regularization nonnegative matrix factorization

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Community Detection in Complex Networks Using nonnegative matrix factorization and Density-Based Clustering Algorithm

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NEURAL PROCESSING LETTERS 2020年第2期51卷 1731-1748页

作者： Lu, Hong Zhao, Qinghua Sang, Xiaoshuang Lu, Jianfeng Nanjing Univ Sci & Technol Sch Comp Sci Xiaolingwei 200 Nanjing 210094 Peoples R China Nanjing Univ Finance & Econ Coll Informat Engn Nanjing 210023 Peoples R China

Community detection is a critical issue in the field of complex networks. Capable of extracting inherent patterns and structures in high dimensional data, the non-negative matrix factorization (NMF) method has become one of the hottest research topics in community detection recently. However, this method has a significant drawback;most community detection methods using NMF require the number of communities to be preassigned or determined by searching for the best community structure among all candidates. To address the problem, in this paper, we use an improved density peak clustering to obtain the number of cores as the pre-defined parameter of nonnegative matrix factorization. Then we adopt nonnegative double singular value decomposition initialization which can rapidly reduce the approximation error of nonnegative matrix factorization. Finally, we compare and analyze the performance of different algorithms on artificial networks and real-world networks. Experimental results indicate that the proposed method is superior to the state-of-the-art methods.

关键词： Community detection nonnegative matrix factorization Density peak clustering NNDSVD

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GNMFLMI: Graph Regularized nonnegative matrix factorization for Predicting LncRNA-MiRNA Interactions

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IEEE ACCESS 2020年 8卷 37578-37588页

作者： Wang, Mei-Neng You, Zhu-Hong Li, Li-Ping Wong, Leon Chen, Zhan-Heng Gan, Cheng-Zhi Yichun Univ Sch Math & Comp Sci Yichun 336000 Peoples R China Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China Xinjiang Agr Univ Coll Grassland & Environm Sci Urumqi 830052 Peoples R China

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been involved in various biological processes. Emerging evidence suggests that the interactions between lncRNAs and miRNAs play an important role in the regulation of genes and the development of many diseases. Due to the limited scale of known lncRNA-miRNA interactions, and expensive time and labor costs for identifying them by biological experiments, more accurate and efficient lncRNA-miRNA interaction computational prediction approach urgently need to be developed. In this work, we proposed a novel computational model, GNMFLMI, to predict lncRNA-miRNA interactions using graph regularized nonnegative matrix factorization. More specifically, the similarities both lncRNA and miRNA are calculated based on known interaction information and their sequence information. Then, the affinity graphs for lncRNAs and miRNAs are constructed using the p-nearest neighbors, respectively. Finally, a graph regularized nonnegative matrix factorization model is developed to accurately infer potential interactions between lncRNAs and miRNAs. To assess the performance of GNMFLMI, five-fold cross-validation experiments are carried out. The AUC values achieved by GNMFLMI on two datasets are 0.9769 and 0.8894, respectively, which outperform the compared methods. In the case studies for lncRNA nonhsat159254.1 and miRNA hsa-mir-544a, 20 and 16 of the top-20 associations predicted by GNMFLMI are confirmed, respectively. Rigorous experimental results demonstrate that GNMFLMI can effectively predict novel lncRNA-miRNA interactions, which can provide guidance for relevant biomedical research. The source code of GNMFLMI is freely available at https://***/ haichengyi/GNMFLMI.

关键词： Graph regularization lncRNA-miRNA interaction lncRNA-miRNA similarity nonnegative matrix factorization

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Community detection method using improved density peak clustering and nonnegative matrix factorization

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NEUROCOMPUTING 2020年 415卷 247-257页

作者： Lu, Hong Shen, Zhu Sang, Xiaoshuang Zhao, Qinghua Lu, Jianfeng Nanjing Univ Sci & Technol Sch Comp Sci Xiaolingwei 200 Nanjing 210094 Peoples R China Stanford Univ Stat Dept Stanford CA 94305 USA Nanjing Univ Finance & Econ Coll Informat Engn Nanjing 210023 Peoples R China

Community detection in networks is valuable in analyzing, designing, and optimizing complex network. Recently, the nonnegative matrix factorization (NMF) method has successfully uncovered the community structure in the complex networks. However, most of community detection methods based on NMF require the number of the community as a prior information. To address this problem, in this paper, we use the improved density peak clustering (DPC) to obtain the number of centers as the preassigned parameter for nonnegative matrix factorization. The proposed algorithm first calculates the modified PageRank of nodes as the density indexes and then draws a decision graph to obtain the hubs of the network. By means of Markov chain model of a random walk, we execute NMF on one expansion feature matrix. Finally, we compare and analyze the performance of different algorithms on artificial networks and real-world networks. Experimental results indicate that the proposed method is superior to the state-of-the-art methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Community detection nonnegative matrix factorization Density peak clustering Random walk Amendatory PageRank

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Semi-Supervised nonnegative matrix factorization of Wide-Field Fluorescence Microscopic Images for Tissue Diagnosis

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MICROSCOPY AND MICROANALYSIS 2020年第3期26卷 419-428页

作者： Soman, Shania M. Rekha, Charuvil Radhakrishna Pillai Santhakumar, Hema Narendrakumar, Uttamchand Jayasree, Ramapurath S. VIT Univ Sch Elect & Engn Vellore 632014 Tamil Nadu India Sree Chitra Tirunal Inst Med Sci & Technol Div Biophoton & Imaging Trivandrum 695012 Kerala India VIT Univ Sch Mech Engn Vellore 632014 Tamil Nadu India

This study tests the use of a constrained nonnegative matrix factorization (NMF) algorithm to explore the comparatively new field of chemometric microscopy to support tissue diagnosis. The algorithm can extract the spectral signature and the absolute concentration map of endogenous fluorophores from wide-field microscopic images. The resultant data distinguished normal and fibrous calvarial tissues, based on the changes in their spectral signatures. The absolute concentration map of endogenous fluorophores, nicotinamide adenine dinucleotide (NADH), flavin adenine dinucleotide (FAD), and lipofuscin were derived from microscopic images and compared with the fluorescence from pure fluorophores. While the absolute concentration of NADH increased, the same of FAD and lipofuscin decreased from a normal to fibrous calvarial condition. An increase in the optical redox ratio, possibly due to the metabolic changes during the development of fibrosis, was observed. Differentiating tissue types using the absolute concentration map was found to be considerably more precise than that achievable with relative concentration. The quantification of fluorophores with reference to the absolute concentration map can eliminate uncertainties due to system responses or measurement details, thereby generating more biologically apposite data. Wide-field microscopy augmented with a constrained NMF algorithm could emerge as an advanced diagnostic tool, potentially heralding the emergence of chemometric microscopy.

关键词： chemometric microscopy endogenous fluorescence nonnegative matrix factorization optical redox ratio spatial distribution spatial diversity

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Safety Monitoring by a Graph-Regularized Semi-Supervised nonnegative matrix factorization With Applications to a Vision-Based Marking Process

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IEEE ACCESS 2020年 8卷 112278-112286页

作者： Fan, Song Jia, Qilong Cheng, Wan Sheng Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

This paper proposes a brand-new method to perform safety monitoring using images for steel coil marking industrial processes. The new safety monitoring method is developed with the aid of a new graph-regularized semi-supervised nonnegative matrix factorization (GSNMF) algorithm. Compared with the existing nonnegative matrix factorization (NMF)-like algorithms, GSNMF is developed in an all-new manner so that it not only can take advantage of images with known labels and images with unknown labels to train a model for monitoring purpose, but also can take advantage of graph theory to improve the monitoring performance. Because any two different samples are connected by an edge in a graph, thus graph theory is beneficial for GSNMF to measure the similarity between any two different samples and to assign the same labels for the samples with close connections between them. As a result, GSNMF is more capable of analyzing the samples with a complicated distribution than the existing NMF-like algorithms, theoretically. Finally, an experiment on a steel coil marking process is adopted to evaluate the superiorities of our proposed method over the existing methods.

关键词： Fault detection nonnegative matrix factorization semi-supervised learning marking process

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Bayesian nonnegative matrix factorization With Dirichlet Process Mixtures

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 3860-3870页

作者： Li, Caoyuan Xie, Hong-Bo Mengersen, Kerrie Fan, Xuhui Da Xu, Richard Yi Sisson, Scott A. Van Huffel, Sabine Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Queensland Univ Technol ARC Ctr Excellence Math & Stat Frontiers Brisbane Qld 4001 Australia Univ New South Wales Sch Math & Stat Sydney NSW 2033 Australia Katholieke Univ Leuven ESAT Stadius Div Dept Elect Engn B-3001 Leuven Belgium

nonnegative matrix factorization (NMF) is valuable in many applications of blind source separation, signal processing and machine learning. A number of algorithms that can infer nonnegative latent factors have been developed, but most of these assume a specific noise kernel. This is insufficient to deal with complex noise in real scenarios. In this paper, we present a hierarchical Dirichlet process nonnegative matrix factorization (DPNMF) model in which the Gaussian mixture model is used to approximate the complex noise distribution. Moreover, the model is cast in the nonparametric Bayesian framework by using Dirichlet process mixture to infer the necessary number of Gaussian components. We derive a mean-field variational inference algorithm for the proposed nonparametric Bayesian model. We first test the model on synthetic data sets contaminated by Gaussian, sparse and mixed noise. We then apply it to extract muscle synergies from the electromyographic (EMG) signal and to select discriminative features for motor imagery single-trial electroencephalogram (EEG) classification. Experimental results demonstrate that DPNMF performs better in extracting the latent nonnegative factors in comparison with state-of-the-art methods.

关键词： Dirichlet process nonnegative matrix factorization nonparametric Bayesian methods Gaussian mixture model variational Bayes

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