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12th International Conference on Advanced Computational Intelligence (ICACI)

作者： Li, Xinqi Wang, Jun Kwong, Sam City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Institude Shenzhen Peoples R China

ISBN: (数字)9781728142487

ISBN: (纸本)9781728142487

sparse nonnegative matrix factorization (SNMF) attracts much attention in the past two decades because its sparse and part-based representations are desirable in many machine learning applications. Due to the combinatorial nature of the sparsity constraint in form of l(0)-norm, the problem is hard to solve. In this paper, a hyperbolic tangent function is introduced to approximate the l(0)-norm. A discrete-time neurodynamic approach is developed for solving the proposed formulation. The stability and the convergence behavior are shown for the state vectors. Experiment results are discussed to demonstrate the superiority of the approach. The results show that this approach outperforms other sparse NMF approaches with the smallest relative reconstruction error and the required level of sparsity.

关键词： neurodynamic optimization sparse nonnegative matrix factorization

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sparse nonnegative matrix factorization Based on Collaborative Neurodynamic Optimization 9

Sparse Nonnegative Matrix Factorization Based on Collaborati...

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9th International Conference on Information Science and Technology (ICIST)

作者： Che, Hangjun Wang, Jun City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen Hong Kong Peoples R China City Univ Hong Kong Sch Data Sci Hong Kong Peoples R China

ISBN: (纸本)9781728121062

This paper presents a collaborative neurodynamic approach to sparse nonnegative matrix factorization (SNMF). SNMF is formulated as a bilevel optimization problem. In the lower level of the problem, the sparsity of factorized matrix is minimized subject to the factorization error and nonnegative constraints. In the upper level of the problem, the parameter of the inverted Gaussian function is minimized to approximate l(0) norm. A group of neurodynamic models operating at two timescales is employed to solve the reformulated problem. The experimental results show the superiority of the proposed approach.

关键词： sparse nonnegative matrix factorization Bilevel optimization Collaborative neurodynamic approach

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Clustering-based hyperspectral band selection using sparse nonnegative matrix factorization

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Journal of Zhejiang University-Science C(Computers and Electronics) 2011年第7期12卷 542-549页

作者： Ji-ming LI 1,2,Yun-tao QIAN 1 (1 School of Computer Science and Technology,Zhejiang University,Hangzhou 310027,China) (2 Zhejiang Police College,Hangzhou 310053,China) School of Computer Science and Technology Zhejiang University Zhejiang Police College

Hyperspectral imagery generally contains a very large amount of data due to hundreds of spectral *** selection is often applied firstly to reduce computational cost and facilitate subsequent tasks such as land-cover classification and higher level image *** this paper,we propose a new band selection algorithm using sparse nonnegative matrix factorization (sparse NMF).Though acting as a clustering method for band selection,sparse NMF need not consider the distance metric between different spectral bands,which is often the key step for most common clustering-based band selection *** imposing sparsity on the coefficient matrix,the bands' clustering assignments can be easily indicated through the largest entry in each column of the *** results showed that sparse NMF provides considerable insight into the clustering-based band selection problem and the selected bands are good for land-cover classification.

关键词： Hyperspectral Band selection Clustering sparse nonnegative matrix factorization

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Band selection using sparse nonnegative matrix factorization with the thresholded Earth's mover distance for hyperspectral imagery classification

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EARTH SCIENCE INFORMATICS 2015年第4期8卷 907-918页

作者： Sun, Weiwei Li, Weiyue Li, Jialin Lai, Yenming Mark Ningbo Univ Coll Architectural Engn Civil Engn & Environm Ningbo 315211 Zhejiang Peoples R China Shanghai Normal Univ Inst Urban Dev Shanghai 200234 Peoples R China Univ Texas Austin Inst Computat Engn & Sci Austin TX 78712 USA

A sparse nonnegative matrix factorization method with the thresholded ground distance (SNMF-TEMD) is proposed to solve the band selection problem in hyperspectral imagery (HSI) classification. The SNMF-TEMD assumes that band vectors are sampled from a union of low-dimensional subspaces and approximates a HSI data matrix with the product of a basis matrix constructed from subspaces and a sparse coefficient matrix. The SNMF-TEMD utilizes the TEMD metric to better measures approximation errors during the optimization of HSI data factorization. The TEMD metric makes up the theoretical drawbacks in the Euclidean distance (ED) and Kullback-Leibler divergence (KLD) metrics when measuring the approximation errors in HSI datasets. The SNMF-TEMD is solved by the combination of min-cost-flow algorithm and multiplicative update rules. The band cluster assignments are found according to positions of largest entries in columns of the coefficient matrix and the desired band subset constitutes with the bands closest to their cluster centers. Three groups of experiments on two HSI datasets are performed to explore the performance of SNMF-TEMD. Four popular band selection methods are used to make comparisons: affinity propagation (AP), maximum-variance principal component analysis (MVPCA), SNMF with ED metric (SNMF-ED) and SNMF with KLD metric (SNMF-KLD). Experimental results show that SNMF-TEMD outperforms all four methods in classification accuracy and its computational speed is slower than SNMF-ED and SNMF-KLD. SNMF-TEMD is a better choice for band selection among all five methods because of its overwhelming advantage in classification and the popular speed remedy scheme from parallel computing and high-performance computers.

关键词： Band selection sparse nonnegative matrix factorization Thresholded earth's mover distance Hyperspectral imagery Classification

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Data Classification with Ensembles of One-Class Support Vector Machines and sparse nonnegative matrix factorization 7

Data Classification with Ensembles of One-Class Support Vect...

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7th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Cyganek, Boguslaw Krawczyk, Bartosz AGH Univ Sci & Technol Al Mickiewicza 30 PL-30059 Krakow Poland Wroclaw Univ Technol PL-50370 Wroclaw Poland

ISBN: (纸本)9783319157023;9783319157016

The paper presents a method for data classification with ensemble of one-class classifiers based on data segmentation. Each data class is partitioned with the nonnegative matrix factorization (NMF) algorithm with sparse constraints. It allows splitting of the input data into compact and consistent data clusters with automatic determination of a number of clusters. Data partitions are fed to an ensemble composed of a number of one-class support vector machine (SVM) classifiers. The proposed method shows high accuracy and fast classification.

关键词： Ensemble of classifiers One-class support vector machine sparse nonnegative matrix factorization

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ADAPTIVE ENDMEMBER EXTRACTION BASED sparse nonnegative matrix factorization WITH SPATIAL LOCAL INFORMATION

ADAPTIVE ENDMEMBER EXTRACTION BASED SPARSE NONNEGATIVE MATRI...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Li, Huali Li, Shutao Zhang, Liangpei Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R Wuhan Peoples R China

ISBN: (纸本)9781479979295

Hyperspectral Unmixing aims at getting the endmember signature and their corresponding abundance maps from highly mixed Hyperspectral image. nonnegative matrix factorization (NMF) is a widely used method for spectral unmixing because it can obtain better performance while there is no pure pixels in the hyperspectral image. However, many methods based on nonnegative matrix factorization seldom consider the spatial information both on local and nonlocal. To combine the spatial and spectral information together to improve the unmixing accuracy, an adaptive endmember extraction based sparse nonnegative matrix factorization with spatial local information (ASNMF) is proposed in this paper. A superpixel segmentation is to obtain many meaningful regions which are spectral similar and spatial adjacent. Endmember is adaptively extracted on each superpixel to generate endmember set. Initialing the endmember set, ASNMF could adaptively obtain the final endmembers with the sparse nonnegative matrix factorization. Both the experiments on synthetic and real scene images show the effectiveness of the proposed method for hyperspectral unmixing.

关键词： adaptive endmember extraction sparse nonnegative matrix factorization hyperspectral remote sensing imagery

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ADAPTIVE ENDMEMBER EXTRACTION BASED sparse nonnegative matrix factorization WITH SPATIAL LOCAL INFORMATION

ADAPTIVE ENDMEMBER EXTRACTION BASED SPARSE NONNEGATIVE MATRI...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Huali Li Shutao Li Liangpei Zhang Coll. of Electr. & Inf. Eng. Hunan Univ. Changsha China

ISBN: (纸本)9781479979301

关键词： geophysical image processing geophysical techniques hyperspectral imaging matrix decomposition ASNMF adaptive endmember extraction endmember set endmember signature hyperspectral image unmixing matrix factorization sparse nonnegative matrix factorization spatial information spatial local information spectral information superpixel segmentation Data mining Hyperspectral imaging Image segmentation Mathematical model sparse matrices hyperspectral remote sensing imagery matrix decomposition hyperspectral imagery sparse matrix geophysical techniques image segmentation nonnegative data mining Mathematical Model exsolution hyperspectral remote sensing local information.

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On the use of sparse signal decomposition in the analysis of multi-channel surface electromyograms

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SIGNAL PROCESSING 2006年第3期86卷 603-623页

作者： Theis, FJ García, GA Univ Regensburg Inst Biophys D-93040 Regensburg Germany Osaka Univ Dept Bioinformat Engn Osaka Japan

The decomposition of surface electromyogram data sets (s-EMG) is studied using blind source separation techniques based on sparseness;namely independent component analysis, sparse nonnegative matrix factorization, and sparse component analysis. When applied to artificial signals we find noticeable differences of algorithm performance depending on the source assumptions. In particular, sparse nonnegative matrix factorization outperforms the other methods with regard to increasing additive noise. However, in the case of real s-EMG signals we show that despite the fundamental differences in the various models, the methods yield rather similar results and can successfully separate the source signal. This can be explained by the fact that the different sparseness assumptions (super-Gaussianity, positivity together with minimal l-norm and fixed number of zeros, respectively) are all only approximately fulfilled thus apparently forcing the algorithms to reach similar results, but from different initial conditions. (C) 2005 Elsevier B.V. All rights reserved.

关键词： surface EMG blind source separation sparse component analysis independent component analysis sparse nonnegative matrix factorization

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Inertial Proximal Alternating Linearized Minimization (iPALM) for Nonconvex and Nonsmooth Problems

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SIAM JOURNAL ON IMAGING SCIENCES 2016年第4期9卷 1756-1787页

作者： Pock, Thomas Sabach, Shoham Graz Univ Technol Inst Comp Graph & Vis A-8010 Graz Austria AIT Austrian Inst Technol GmbH Digital Safety & Secur Dept A-1220 Vienna Austria Technion Israel Inst Technol Dept Ind Engn & Management IL-3200003 Haifa Israel

In this paper we study nonconvex and nonsmooth optimization problems with semialgebraic data, where the variables vector is split into several blocks of variables. The problem consists of one smooth function of the entire variables vector and the sum of nonsmooth functions for each block separately. We analyze an inertial version of the proximal alternating linearized minimization algorithm and prove its global convergence to a critical point of the objective function at hand. We illustrate our theoretical findings by presenting numerical experiments on blind image deconvolution, on sparse nonnegative matrix factorization and on dictionary learning, which demonstrate the viability and effectiveness of the proposed method.

关键词： alternating minimization blind image deconvolution block coordinate descent heavy ball method Kurdyka-Lojasiewicz property nonconvex and nonsmooth minimization sparse nonnegative matrix factorization dictionary learning

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Proximal alternating linearized minimization for nonconvex and nonsmooth problems

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MATHEMATICAL PROGRAMMING 2014年第1-2期146卷 459-494页

作者： Bolte, Jerome Sabach, Shoham Teboulle, Marc Univ Toulouse 1 GREMAQ TSE Manufacture Tabacs F-31015 Toulouse France Tel Aviv Univ Sch Math Sci IL-69978 Tel Aviv Israel

We introduce a proximal alternating linearized minimization (PALM) algorithm for solving a broad class of nonconvex and nonsmooth minimization problems. Building on the powerful Kurdyka-Aojasiewicz property, we derive a self-contained convergence analysis framework and establish that each bounded sequence generated by PALM globally converges to a critical point. Our approach allows to analyze various classes of nonconvex-nonsmooth problems and related nonconvex proximal forward-backward algorithms with semi-algebraic problem's data, the later property being shared by many functions arising in a wide variety of fundamental applications. A by-product of our framework also shows that our results are new even in the convex setting. As an illustration of the results, we derive a new and simple globally convergent algorithm for solving the sparse nonnegative matrix factorization problem.

关键词： Alternating minimization Block coordinate descent Gauss-Seidel method Kurdyka-Lojasiewicz property Nonconvex-nonsmooth minimization Proximal forward-backward sparse nonnegative matrix factorization

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