Results 21 to 30 of about 4,116 (162)
Quantized nonnegative matrix factorization [PDF]
2014 19th International Conference on Digital Signal Processing, 2014 Even though Nonnegative Matrix Factorization (NMF) in its original form performs rank reduction and signal compaction implicitly, it does not explicitly consider storage or transmission constraints. We propose a Frobenius-norm Quantized Nonnegative Matrix Factorization algorithm that is 1) almost as precise as traditional NMF for decomposition ranks of
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Discriminant projective non-negative matrix factorization. [PDF]
PLoS ONE, 2013 Projective non-negative matrix factorization (PNMF) projects high-dimensional non-negative examples X onto a lower-dimensional subspace spanned by a non-negative basis W and considers W(T) X as their coefficients, i.e., X≈WW(T) X.
Naiyang Guan +4 more
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Block Sparse Symmetric Nonnegative Matrix Factorization Based on Constrained Graph Regularization [PDF]
Jisuanji kexue, 2023 The existing algorithms based on symmetric nonnegative matrix factorization(SymNMF) are mostly rely on initial data to construct affinity matrices,and neglect the limited pairwise constraints,so these methods are unable to effectively distinguish similar
LIU Wei, DENG Xiuqin, LIU Dongdong, LIU Yulan
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Adaptive Kernel Graph Nonnegative Matrix Factorization
Information, 2023 Nonnegative matrix factorization (NMF) is an efficient method for feature learning in the field of machine learning and data mining. To investigate the nonlinear characteristics of datasets, kernel-method-based NMF (KNMF) and its graph-regularized ...
Rui-Yu Li, Yu Guo, Bin Zhang
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Novel Algorithms Based on Majorization Minimization for Nonnegative Matrix Factorization
IEEE Access, 2019 Matrix decomposition is ubiquitous and has applications in various fields like speech processing, data mining and image processing to name a few. Under matrix decomposition, nonnegative matrix factorization is used to decompose a nonnegative matrix into ...
R. Jyothi, Prabhu Babu, Rajendar Bahl
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On Restricted Nonnegative Matrix Factorization
CoRR, 2016 Full version of an ICALP'16 ...
Dmitry Chistikov 0001 +4 more
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Adversarially-Trained Nonnegative Matrix Factorization [PDF]
IEEE Signal Processing Letters, 2021 We consider an adversarially-trained version of the nonnegative matrix factorization, a popular latent dimensionality reduction technique. In our formulation, an attacker adds an arbitrary matrix of bounded norm to the given data matrix. We design efficient algorithms inspired by adversarial training to optimize for dictionary and coefficient matrices ...
Cai, Ting +2 more
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Nonnegative Matrix Factorizations Performing Object Detection and Localization
Applied Computational Intelligence and Soft Computing, 2012 We study the problem of detecting and localizing objects in still, gray-scale images making use of the part-based representation provided by nonnegative matrix factorizations.
G. Casalino, N. Del Buono, M. Minervini
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Computing a Nonnegative Matrix Factorization---Provably [PDF]
SIAM Journal on Computing, 2012 29 pages, 3 ...
Sanjeev Arora +3 more
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Complexity, 2018 We present a novel method, called graph sparse nonnegative matrix factorization, for dimensionality reduction. The affinity graph and sparse constraint are further taken into consideration in nonnegative matrix factorization and it is shown that the ...
Xiangguang Dai +2 more
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