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9th International Conference on Intelligence Science and Big Data Engineering (IScIDE)

作者： Chen, Wen-Sheng Chen, Haitao Pan, Binbin Chen, Bo Shenzhen Univ Coll Math & Stat Shenzhen Peoples R China Shenzhen Univ Guangdong Key Lab Media Secur Shenzhen 518060 Peoples R China

ISBN: (纸本)9783030362041;9783030362034

nonnegative matrix factorization (NMF) is a low-rank decomposition based image representation method under the nonnegativity constraint. However, a lot of NMF based approaches utilize Frobenius-norm or KL-divergence as the metrics to model the loss functions. These metrics are not dilation-invariant and thus sensitive to the scale-change illuminations. To solve this problem, this paper proposes a novel robust NMF method (CSNMF) using cosine similarity induced metric, which is both rotation-invariant and dilation-invariant. The invariant properties are beneficial to improving the performance of our method. Based on cosine similarity induced metric and auxiliary function technique, the update rules of CSNMF are derived and theoretically shown to be convergent. Finally, we empirically evaluate the performance and convergence of the proposed CSNMF algorithm. Compared with the state-of-the-art NMF-based algorithms on face recognition, experimental results demonstrate that the proposed CSNMF method has superior performance and is more robust to the variation of illumination.

关键词： nonnegative matrix factorization Face recognition Cosine similarity induced metric

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Target detection method for reverberant environments in continuous-wave active sonar system based on group multichannel nonnegative matrix factorization 23

Target detection method for reverberant environments in cont...

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23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Lee, Seokjin Kyungpook National University Korea Republic of

ISBN: (纸本)9783939296157

In the underwater environment, the active sonar system emits an acoustic wave and receives echo signals to detect a target. However, the echo signals consist of not only the echo from the target but also the reverberation from scatterers. If the target has a low speed, the detection problem is more difficult because the target echo is interfered by the reverberation severely. In this paper, the target detection method for the reverberant environment is developed based on multichannel nonnegative matrix factorization. To extract the target echo signal mixed with reverberations, received signal is transformed into bearing-time-frequency domain, and analyzed into bearing, frequency, and temporal basis by the multichannel nonnegative matrix factorization. The simulation was performed to evaluate the proposed method, and the result shows that the proposed method has good detection performance in the simulated reverberant environments. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Active Sonar nonnegative matrix factorization Reverberation Target Detection

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How to Apply Random Projections to nonnegative matrix factorization with Missing Entries? 27

How to Apply Random Projections to Nonnegative Matrix Factor...

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27th European Signal Processing Conference (EUSIPCO)

作者： Yahaya, Farouk Puigt, Matthieu Delmaire, Gilles Roussel, Gilles Univ Littoral COte dOpale LISIC EA 4491 F-62228 Calais France

ISBN: (纸本)9789082797039

Random projections belong to the major techniques to process big data and have been successfully applied to nonnegative matrix factorization (NMF). However, they cannot be applied in the case of missing entries in the matrix to factorize, which occurs in many actual problems with large data matrices. In this paper, we thus aim to solve this issue and we propose a novel framework to apply random projections in weighted NMF, where the weight models the confidence in the data (or the absence of confidence in the case of missing data). We experimentally show the proposed framework to significantly speed-up state-of-the-art NMF methods under some mild conditions. In particular, the proposed strategy is particularly efficient when combined with Nesterov gradient or alternating least squares.

关键词： nonnegative matrix factorization missing data random projections low-rank matrix completion blind source separation big data

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Transductive nonnegative matrix Tri-factorization

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IEEE ACCESS 2020年 8卷 81331-81347页

作者： Teng, Xiao Lan, Long Zhang, Xiang Dong, Guohua Luo, Zhigang Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Changsha 410073 Peoples R China Natl Univ Def Technol Inst Quantum Informat Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

nonnegative matrix factorization (NMF) decomposes a nonnegative matrix into the product of two lower-rank nonnegative matrices. Since NMF learns parts-based representation, it has been widely used as a feature learning component in many fields. However, standard NMF algorithms ignore the training labels as well as unlabeled data in the test domain. In this paper, we propose a transductive nonnegative matrix tri-factorization method (T-NMTF) to simultaneously exploit the label information of training examples and the statistical structure of features in the test domain. Different from standard NMF, nonnegative matrix tri-factorization (NMTF) decomposes a nonnegative matrix into the product of three lower-rank nonnegative matrices, and thus provides a flexible framework to transduce discriminative information of training examples to test examples. In particular, the proposed T-NMTF projects both training examples and test examples into a unified subspace, and expects the coefficients of training examples close to their label vectors. Since training examples and test examples are assumed to identically distributed, it is reasonable to expect the learned coefficients of test examples approximate their label vectors well. To estimate the T-NMTF parameters, we develop an efficient multiplicative update rule and prove its convergence. In addition, we propose a manifold regularized T-NMTF (MT-NMTF) algorithm that exploits the local geometry structure of the dataset to boost discriminant power. Experimental results on face recognition demonstrate the effectiveness of T-NMTF and MT-NMTF.

关键词： matrix decomposition Training Robustness Support vector machines Convergence Standards Manifolds nonnegative matrix factorization nonnegative matrix tri-factorization transductive learning

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A study on separation method combined gamma-process nonnegative matrix factorization and deep learning 23

A study on separation method combined gamma-process nonnegat...

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23rd International Congress on Acoustics: Integrating 4th EAA Euroregio, ICA 2019

作者： Jomae, Satoru Ota, Kenko Yoshino, Hideaki Nippon Institute of Technology Japan

ISBN: (纸本)9783939296157

Accurate analysis of fundamental frequency and chord constitutive notes is a hard problem. However, to solve this problem is important for similar music retrieval and music arrangement etc. Development of an accurate method for sound source separation is required to analyze the fundamental frequency etc. accurately. In this research, we propose a method of sound source separation that combines gamma-process non-negative matrix factorization (GaP-NMF) and Deep Neural Network (DNN). In the proposed method, we first estimate the basis with GaP-NMF. Then, DNN classifies the estimated basis according to musical instruments. The basis estimated by GaP-NMF is emphasized by multiplying with the spectrum template of musical instruments which is specified by DNN. We conducted a sound source separation experiment to verify the performance of the proposed method. Sound sources are composed of multiple musical instrument sounds. As a result of separating a single musical instrument sound from the sound sources, we confirmed that the proposed method improved the SNR by 1.1 dB over the conventional method depending on the data. © 2019 Proceedings of the International Congress on Acoustics. All rights reserved.

关键词： Deep Learning nonnegative matrix factorization Sound Source Separation

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Clustering Stability via Concept-based nonnegative matrix factorization 2019

Clustering Stability via Concept-based Nonnegative Matrix Fa...

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3rd International Conference on Machine Learning and Soft Computing (ICMLSC)

作者： Nghia Duong-Trung Minh-Hoang Nguyen Nguyen, Hanh T. H. Can Tho Univ Technol Can Tho Vietnam FPT Univ Can Tho Vietnam Univ Trento Dept Informat Engn & Comp Sci Trento Italy Informat Syst & Machine Learning Lab Hildesheim Germany

ISBN: (纸本)9781450366120

One of the most important contributions of topic modeling is to accurately and the ectively discover and classify documents in a collection of texts by a number of clusters/topics. However, finding an appropriate number of topics is a particularly challenging model selection question. In this context, we introduce a new unsupervised conceptual stability framework to access the validity of a clustering solution. We integrate the proposed framework into nonnegative matrix factorization (NMF) to guide the selection of desired number of topics. Our model provides a exible way to enhance the interpretation of NMF for the effective clustering solutions. The work presented in this paper crosses the bridge between stability-based validation of clustering solutions and NMF in the context of unsupervised learning. We perform a thorough evaluation of our approach over a wide range of real-world datasets and compare it to current state-of-the-art which are two NMF-based approaches and four Latent Dirichlet Allocation (LDA) based models. the quantitative experimental results show that integrating such conceptual stability analysis into NMF can lead to significant improvements in the document clustering and information retrieval the ectiveness.

关键词： Concept Discovery Conceptual Stability Topic Modeling Unsupervised Document Clustering nonnegative matrix factorization

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A Hybrid Secure Watermarking Scheme Using nonnegative matrix factorization and FastWalsh-Hadamard Transform

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JOURNAL OF APPLIED SECURITY RESEARCH 2020年第2期15卷 185-198页

作者： Abdallah, Emad E. Otoom, Ahmed Fawzi Abdallah, Alaa E. Bsoul, Mohammad Awwad, Sari Hashemite Univ Fac Prince Al Hussein Bin Abdallah II Informat Te POB 150459 Zarqa 13115 Jordan

We present a robust and unnoticeable secure image watermarking approach using nonnegative matrix factorization (NMF) and Fast Walsh-Hadamard Transform (FWHT). The core idea of the proposed scheme consist of four key steps: The original cover image is divided into small blocks. The NMF is then applied to each block separately, followed by using the FWHT to the generated weight matrix. Finally, the singular values of the watermark image are distributed over the transformed blocks. The experimental results certainly show enhanced visual imperceptibility and exceptional resiliency against various types of attacks.

关键词： Watermarking fast hadamard transform nonnegative matrix factorization robustness to attacks

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Joint Linear Regression and nonnegative matrix factorization Based on Self-Organized Graph for Image Clustering and Classification

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IEEE ACCESS 2018年 6卷 38820-38834页

作者： Zhu, Wenjie Yan, Yunhui Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Liaoning Peoples R China

nonnegative matrix factorization (NMF) technique has been developed successfully to represent the intuitively meaningful feature of data. A suitable representation can faithfully preserve the intrinsic structure of data. Due to the fact that it introduces the label information, semi-supervised NMF has been demonstrated more advantageous in image representation than original NMF. However, previous semi-supervised NMF variants construct a label indicator matrix only for tagging the labeled data and not being optimized together with the matrix factorization. It is short of label propagation and fails to work for predicting the attribution of data. Moreover, the transductive semi-supervised NMF variants cannot dispose the prediction of unseen data, restricting the application of NMF. In this paper, a joint optimization framework of linear regression and NMF (LR-NMF) based on the self-organized graph is proposed for a completed task which simultaneously takes into account image representation and attribution prediction. By minimizing the proposed objective, three interactive threads are running: decomposing the data into nonnegative basis matrix and the corresponding representation, linear regression using the nonnegative representation, and label propagation based on the self-organized graph which is defined in the feature space. The products of LR-NMF can be viewed as extracting nonnegative feature for clustering, meanwhile, they can be used to solve the out-of-sample problem for classification. Extensive clustering and classification experiments on the digit, face, and object challenging data sets are presented to show the efficacy of the proposed LR-NMF algorithm.

关键词： nonnegative matrix factorization linear regression self-organized graph semi-supervised clustering and classification

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Clustering Multivariate Time Series Data a via Multi-nonnegative matrix factorization in Multi-Relational Networks

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IEEE ACCESS 2018年 6卷 74747-74761页

作者： Zhou, Lihua Du, Guowang Tao, Dapeng Chen, Hongmei Cheng, Jun Gong, Libo Yunnan Univ Sch Informat Kunming 650091 Yunnan Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Yunnan Rural Sci & Technol Serv Ctr Kunming 650051 Yunnan Peoples R China

In multivariate time series clustering, the inter-similarity across distinct variates and the intra-similarity within each variate pose analytical challenges. Here, we propose a novel multivariate time series clustering method using multi-nonnegative matrix factorization (MNMF) in multi-relational networks. Specifically, a set of multivariate time series is transformed from the time-space domain into a multi-relational network in the topological domain. Then, the multi-relational network is factorized to identify time series clusters. The transformation from the time-space domain to the topological domain benefits from the ability of networks to characterize both the local and global relationships between the nodes, and MNMF incorporates inter-similarity across distinct variates into clustering. Furthermore, to trace the evolutionary trends of clusters, time series is transformed into a dynamic multi-relational network, thereby extending MNMF to dynamic MNMF. Extensive experiments illustrate the superiority of our approach compared with the current state-of-the-art algorithms.

关键词： Multivariate time series clustering multi-relational network nonnegative matrix factorization

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Search result diversification on attributed networks via nonnegative matrix factorization

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INFORMATION PROCESSING & MANAGEMENT 2018年第6期54卷 1277-1291页

作者： Meng, Zaiqiao Shen, Hong Huang, Huimin Liu, Wei Wang, Jing Sangaiah, Arun Kumar Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Univ Adelaide Sch Comp Sci Adelaide SA Australia Neusoft Inst Foshan Guangdong Peoples R China VTT Univ Sch Comp Sci & Engn Vellore Tamil Nadu India

Search result diversification is an effective way to tackle query ambiguity and enhance result novelty. In the context of large information networks, diversifying search result is also critical for further design of applications such as link prediction and citation recommendation. In previous work, this problem has mainly been tackled in a way of implicit query intent. To further enhance the performance on attributed networks, we propose a novel search result diversification approach via nonnegative matrix factorization. Our approach encodes latent query intents as well as nodes as representation vectors by a novel nonnegative matrix factorization model, and the diversity of the results accounts for the query relevance and the novelty w.r.t. these vectors. To learn the representation vectors of nodes, we derive the multiplicative updating rules to train the nonnegative matrix factorization model. We perform a comprehensive evaluation on our approach with various baselines. The results show the effectiveness of our proposed solution, and verify that attributes do help improve diversification performance.

关键词： Graph search Diversification nonnegative matrix factorization Attributed network

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