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Multikernel Clustering via non-negative matrix factorization Tailored Graph Tensor Over Distributed Networks

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2021年第7期39卷 1946-1956页

作者： Ren, Zhenwen Mukherjee, Mithun Bennis, Mehdi Lloret, Jaime Southwest Univ Sci & Technol Dept Natl Def Sci & Technol Mianyang 621010 Sichuan Peoples R China Nanjing Univ Sci & Technol Dept Comp Sci Nanjing 210094 Peoples R China Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Univ Oulu Ctr Wireless Commun Oulu 90570 Finland Univ Politecn Valencia Inst Invest Gest Integrada Zonas Costeras IGIC Valencia 46022 Spain Staffordshire Univ Sch Comp & Digital Technol Stoke ST4 2DE England

Next-generation wireless networks are witnessing an increasing number of clustering applications, and produce a large amount of non-linear and unlabeled data. In some degree, single kernel methods face the challenging problem of kernel choice. To overcome this problem for non-linear data clustering, multiple kernel graph-based clustering (MKGC) has attracted intense attention in recent years. However, existing MKGC methods suffer from two common problems: (1) they mainly aim to learn a consensus kernel from multiple candidate kernels, slight affinity graph learning, such that cannot fully exploit the underlying graph structure of non-linear data;(2) they disregard the high-order correlations between all base kernels, which cannot fully capture the consistent and complementary information of all kernels. In this paper, we propose a novel non-negative matrix factorization (NMF) tailored graph tensor MKGC method for non-linear data clustering, namely TMKGC. Specifically, TMKGC integrates NMF and graph learning together in kernel space so as to learn multiple candidate affinity graphs. Afterwards, the high-order structure information of all candidate graphs is captured in a 3-order tensor kernel space by introducing tensor singular value decomposition based tensor nuclear norm, such that an optimal affinity graph can be obtained subsequently. Based on the alternating direction method of multipliers, the effective local and distributed solvers are elaborated to solve the proposed objective function. Extensive experiments have demonstrated the superiority of TMKGC compared to the state-of-the-art MKGC methods.

关键词： Kernel Tensors Correlation Internet of Things Task analysis Linear programming Clustering algorithms Multiple kernel clustering intelligent network distributed computation non-negative matrix factorization tensor learning

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Noise-Robust Voice Conversion Based on Sparse Spectral Mapping Using non-negative matrix factorization

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2014年第6期E97D卷 1411-1418页

作者： Aihara, Ryo Takashima, Ryoichi Takiguchi, Tetsuya Ariki, Yasuo Kobe Univ Grad Sch Syst Informat Kobe Hyogo 6578501 Japan Kobe Univ Org Adv Sci & Technol Kobe Hyogo 6578501 Japan

This paper presents a voice conversion (VC) technique for noisy environments based on a sparse representation of speech. Sparse representation-based VC using non-negative matrix factorization (NMF) is employed for noise-added spectral conversion between different speakers. In our previous exemplar-based VC method, source exemplars and target exemplars are extracted from parallel training data, having the same texts uttered by the source and target speakers. The input source signal is represented using the source exemplars and their weights. Then, the converted speech is constructed from the target exemplars and the weights related to the source exemplars. However, this exemplar-based approach needs to hold all training exemplars (frames), and it requires high computation times to obtain the weights of the source exemplars. In this paper, we propose a framework to train the basis matrices of the source and target exemplars so that they have a common weight matrix. By using the basis matrices instead of the exemplars, the VC is performed with lower computation times than with the exemplar-based method. The effectiveness of this method was confirmed by comparing its effectiveness (in speaker conversion experiments using noise-added speech data) with that of an exemplar-based method and a conventional Gaussian mixture model (GMM)-based method.

关键词： voice conversion sparse representation non-negative matrix factorization noise robustness

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Comparison of Convolutive Kernel Compensation and non-negative matrix factorization of Surface Electromyograms

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2018年第10期26卷 1935-1944页

作者： Savc, Martin Glaser, Vojko Kranjec, Jernej Cikajlo, Imre Matjacic, Zlatko Holobar, Ales Univ Maribor Fac Elect Engn & Comp Sci SLO-2000 Maribor Slovenia Univ Rehabil Inst Ljubljana 1000 Slovenia

We compared non-negative matrix factorization (NMF) and convolution kernel compensation techniques for high-density electromyogram decomposition. The experimental data were recorded from nine healthy persons during controlled single degree of freedom (DOF) wrist flexion-extension, supination-pronation, and ulnar-radial deviation movements. We assembled the identified motor units and NMF components into three groups. Those active mostly during the first and the second movement direction per DOF were placed in the G1 and G3 groups, respectively. The remaining components were nonspecific for movement direction and were placed in the G2 group. In ulnar and radial deviation, the relative energies of identified cumulative motor unit spike trains (CSTs) and NMF components were similarly distributed among the groups. In other two movement types, the energy of NMF components in the G2 group was significantly larger than the energy of CSTs. We further performed a coherence analysis between CSTs and sums of NMF components in each group. Both decompositions demonstrated a solid match, but only at frequencies <3 Hz. At higher frequencies, the coherence hardly exceeded the value of 0.5. Potential reasons for these discrepancies include the negative impact of motor unit action potential shapes and noise on NMF decomposition.

关键词： High-density surface EMG non-negative matrix factorization convolution kernel compensation wrist movements motor unit identification

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Acoustic Event Classification using spectral band selection and non-negative matrix factorization-based features

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 46卷 77-86页

作者： Ludena-Choez, Jimmy Gallardo-Antolin, Ascension Univ Carlos III Madrid Dept Signal Theory & Commun Leganes 28911 Spain Univ Catolica San Pablo Fac Ingn & Comp Barrio De San Lazaro Arequipa Peru

Feature extraction methods for sound events have been traditionally based on parametric representations specifically developed for speech signals, such as the well-known Mel Frequency Cepstrum Coefficients (MFCC). However, the discrimination capabilities of these features for Acoustic Event Classification (AEC) tasks could be enhanced by taking into account the spectro-temporal structure of acoustic event signals. In this paper, a new front-end for AEC which incorporates this specific information is proposed. It consists of two different stages: short-time feature extraction and temporal feature integration. The first module aims at providing a better spectral representation of the different acoustic events on a frame-by-frame basis, by means of the automatic selection of the optimal set of frequency bands from which cepstral-like features are extracted. The second stage is designed for capturing the most relevant temporal information in the short-time features, through the application of non-negative matrix factorization (NMF) on their periodograms computed over long audio segments. The whole front-end has been evaluated in clean and noisy conditions. Experiments show that the removal of certain frequency bands (which are mainly located in the medium region of the spectrum for clean conditions and in low frequencies for noisy environments) in the short-time feature computation process in conjunction with the NMF technique for temporal feature integration improves significantly the performance of a Support Vector Machine (SVM) based AEC system with respect to the use of conventional MFCCs. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Acoustic Event Classification Feature extraction Temporal feature integration Feature selection Mutual information non-negative matrix factorization

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Topic sense induction from social tags based on non-negative matrix factorization

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INFORMATION SCIENCES 2014年 280卷 16-25页

作者： Chen, Junpeng Feng, Shuai Liu, Juan Wuhan Univ Sch Comp Wuhan 430072 Peoples R China Nanjing Univ Finance & Econ Sch Informat Engn Nanjing Jiangsu Peoples R China

Social tagging, also noted as collaborative tagging or folksonomy, is an important way for users themselves to describe resources on the Web. The tags that the web users adopt to describe the resources are called social tags, and they have been widely used and studied. However, for the absence of a central controlled vocabulary, the semantics of the social tags are ambiguous due to constant changes of either the users' interests or the informal definitions, which makes it hard to directly make use of these social tags in the web applications. In this paper, we propose a non-negative matrix factorization (NMF) based method to automatically induce topic senses from social tags, which can then be used for the tag disambiguation. A novel automatic evaluation method is also proposed to evaluate our method. The experiment results show that the proposed topic sense induction method can help to provide precise resources search and recommendation, which is one of the key functionalities in social tagging systems. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Topic sense induction Social tag Disambiguation non-negative matrix factorization

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Using non-negative matrix factorization in the "unmixing" of diffuse reflectance spectra

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MARINE GEOLOGY 2007年第1-4期241卷 63-78页

作者： Heslop, D. von Dobeneck, T. Hoecker, M. Univ Bremen FB Geowissensch D-28334 Bremen Germany Univ Bremen Res Ctr Ocean Margins D-28334 Bremen Germany

Diffuse reflectance spectrophotometry (DRS), often termed simply color scanning, is a technique applied commonly to marine sediments to provide records of compositional variations. Measured DRS spectra, however, represent the bulk response of a sediment's constituents and are therefore difficult to interpret in their raw form. A quantification technique will be proposed and discussed which approaches the analysis of DRS data sets as a linear mixing problem and applies a non-negative matrix factorization (NMF) algorithm in their decomposition. The presented methodology allows the spectra of the end-member sediment constituents and their fractional abundances to be determined using only the measured data set. Unlike other DRS data processing techniques, NMF only allows additive combinations of end-members to explain the data set and will therefore return only positive abundances. Analysis of sediments from the eastern Mediterranean Sea demonstrates the applicability of the NMF approach and the new method of Orbital Cycle Stacking establishes that the "unmixed" data is consistent with expected environmental change. (C) 2007 Elsevier B.V. All rights reserved.

关键词： spectrophotometry non-negative matrix factorization Mediterranean

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A nonlinear orthogonal non-negative matrix factorization approach to subspace clustering

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PATTERN RECOGNITION 2018年 82卷 40-55页

作者： Tolic, Dijana Antulov-Fantulin, Nino Kopriva, Ivica Rudjer Boskovic Inst Lab Machine Learning & Knowledge Representat Bijenicka Cesta 54 Zagreb 10000 Croatia Swiss Fed Inst Technol Swiss Fed Inst Technol COSS CLU Clausiusstr 50 CH-8092 Zurich Switzerland

A recent theoretical analysis shows the equivalence between non-negative matrix factorization (NMF) and spectral clustering based approach to subspace clustering. As NMF and many of its variants are essentially linear, we introduce a nonlinear NMF with explicit orthogonality and derive general kernel-based orthogonal multiplicative update rules to solve the subspace clustering problem. In nonlinear orthogonal NMF framework, we propose two subspace clustering algorithms, named kernel-based non-negative subspace clustering KNSC-Ncut and KNSC-Rcut and establish their connection with spectral normalized cut and ratio cut clustering. We further extend the nonlinear orthogonal NMF framework and introduce a graph regularization to obtain a factorization that respects a local geometric structure of the data after the nonlinear mapping. The proposed NMF-based approach to subspace clustering takes into account the nonlinear nature of the manifold, as well as its intrinsic local geometry, which considerably improves the clustering performance when compared to the several recently proposed state-of-the-art methods. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Subspace clustering non-negative matrix factorization Orthogonality Kernels Graph regularization

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Semi-Supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical non-negative matrix factorization with Automatic Model Selection

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ACM TRANSACTIONS ON PRIVACY AND SECURITY 2023年第4期26卷 1-27页

作者： Eren, Maksim E. Bhattarai, Manish Joyce, Robert J. Raff, Edward Nicholas, Charles Alexandrov, Boian S. Los Alamos Natl Lab Adv Res Cyber Syst MS B264 Los Alamos NM 87545 USA Los Alamos Natl Lab Theoret Div MS B221 Los Alamos NM 87545 USA Booz Allen Hamilton Machine Learning Res Grp 304 Sentinel Dr Annapolis Jct MD 20701 USA Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21228 USA UMBC Dept Comp Sci & Elect Engn 1000 Hilltop Circle Baltimore MD 21250 USA

Identification of the family to which a malware specimen belongs is essential in understanding the behavior of the malware and developing mitigation strategies. Solutions proposed by prior work, however, are often not practicable due to the lack of realistic evaluation factors. These factors include learning under class imbalance, the ability to identify new malware, and the cost of production-quality labeled data. In practice, deployed models face prominent, rare, and new malware families. At the same time, obtaining a large quantity of up-to-date labeled malware for training a model can be expensive. In this article, we address these problems and propose a novel hierarchical semi-supervised algorithm, which we call the HNMFk Classifier, that can be used in the early stages of the malware family labeling process. Our method is based on non-negative matrix factorization with automatic model selection, that is, with an estimation of the number of clusters. With HNMFk Classifier, we exploit the hierarchical structure of the malware data together with a semi-supervised setup, which enables us to classify malware families under conditions of extreme class imbalance. Our solution can perform abstaining predictions, or rejection option, which yields promising results in the identification of novel malware families and helps with maintaining the performance of the model when a low quantity of labeled data is used. We perform bulk classification of nearly 2,900 both rare and prominent malware families, through static analysis, using nearly 388,000 samples from the EMBER-2018 corpus. In our experiments, we surpass both supervised and semi-supervised baseline models with an F1 score of 0.80.

关键词： Malware malware families non-negative matrix factorization semi-supervised hierarchical model selection class imbalance abstaining prediction reject-option

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Identifying group discriminative and age regressive sub-networks from DTI-based connectivity via a unified framework of non-negative matrix factorization and graph embedding

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MEDICAL IMAGE ANALYSIS 2014年第8期18卷 1337-1348页

作者： Ghanbari, Yasser Smith, Alex R. Schultz, Robert T. Verma, Ragini Univ Penn Ctr Biomed Image Comp & Analyt Perelman Sch Med Dept Radiol Philadelphia PA 19104 USA Childrens Hosp Philadelphia Ctr Autism Res Dept Pediat Philadelphia PA 19104 USA

Diffusion tensor imaging (DTI) offers rich insights into the physical characteristics of white matter (WM) fiber tracts and their development in the brain, facilitating a network representation of brain's traffic pathways. Such a network representation of brain connectivity has provided a novel means of investigating brain changes arising from pathology, development or aging. The high dimensionality of these connectivity networks necessitates the development of methods that identify the connectivity building blocks or sub-network components that characterize the underlying variation in the population. In addition, the projection of the subject networks into the basis set provides a low dimensional representation of it, that teases apart different sources of variation in the sample, facilitating variation-specific statistical analysis. We propose a unified framework of non-negative matrix factorization and graph embedding for learning sub-network patterns of connectivity by their projective non-negative decomposition into a reconstructive basis set, as well as, additional basis sets representing variational sources in the population like age and pathology. The proposed framework is applied to a study of diffusion-based connectivity in subjects with autism that shows localized sparse sub-networks which mostly capture the changes related to pathology and developmental variations. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Diffusion MRI Connectivity analysis non-negative matrix factorization Graph embedding Autism and development

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Robust local-coordinate non-negative matrix factorization with adaptive graph for robust clustering

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INFORMATION SCIENCES 2022年 610卷 1058-1077页

作者： Tang, Jiayi Feng, Hui Wuhan Univ Sch Math & Stat Wuhan 430072 Peoples R China

With its unique geometric properties, non-negative matrix factorization (NMF) has become one of the widely used clustering methods in the field of data mining. Regrettably, most existing NMF methods are sensitive to super-noise (super-outliers). This paper proposes a novel robust clustering method to address this issue. Based on the Hx loss function, this method establishes a novel robust adaptive local structure learning strategy, reducing the interference of noise (outliers) on data reconstruction and space exploration. In addition, a new orthogonal regularization term is incorporated into the model, ensuring the orthogo-nality of the factor matrix and enhancing the discriminant ability. Finally, we develop an efficient algorithm to solve the resultant model and analyze its convergence from theoret-ical and experimental aspects. Experimental results on random synthetic data sets and benchmark databases demonstrate that the proposed method outperforms the existing robust NMF methods in terms of spatial structure learning, discriminant power, and robustness.(c) 2022 Elsevier Inc. All rights reserved.

关键词： non-negative matrix factorization Robust adaptive local structure learning strategy Robust model Clustering

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