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Orthogonal Nonnegative Matrix Factorization by Sparsity and Nuclear Norm Optimization
SIAM Journal on Matrix Analysis and Applications, 2018 © 2018 Society for Industrial and Applied Mathematics. In this paper, we study orthogonal nonnegative matrix factorization. We demonstrate the coefficient matrix can be sparse and low-rank in the orthogonal nonnegative matrix factorization.
Junjun Pan, Michael K Ng
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Constrained Nonnegative Matrix Factorization for Image Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012 Nonnegative matrix factorization (NMF) is a popular technique for finding parts-based, linear representations of nonnegative data. It has been successfully applied in a wide range of applications such as pattern recognition, information retrieval, and ...
Haifeng Liu, Deng Cai, Xuelong Li
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Nonnegative matrix factorization with local similarity learning
Information Sciences, 2021Chong Peng +2 more
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Robust Manifold Nonnegative Matrix Factorization
ACM Transactions on Knowledge Discovery From Data, 2014Feiping Nie, Heng Huang, Chris Ding
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Graph Regularized Nonnegative Matrix Factorization for Data Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011Deng Cai, Xiaofei He, Jiawei Han
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Algorithms and applications for approximate nonnegative matrix factorization
Computational Statistics and Data Analysis, 2007Michael W Berry +2 more
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Document clustering using nonnegative matrix factorization
Information Processing and Management, 2006Michael W Berry, Robert J Plemmons
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SVD based initialization: A head start for nonnegative matrix factorization
Pattern Recognition, 2008Efstratios Gallopoulos
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Nonsmooth nonnegative matrix factorization (nsNMF)
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006Alberto Pascual-Montano +2 more
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