Results 31 to 40 of about 32,711 (308)
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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Multi-Component Nonnegative Matrix Factorization [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Real data are usually complex and contain various components. For example, face images have expressions and genders. Each component mainly reflects one aspect of data and provides information others do not have. Therefore, exploring the semantic information of multiple components as well as the diversity among them is of great benefit to understand ...
Wang, Jing +8 more
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Monotonous (semi-)nonnegative matrix factorization [PDF]
Proceedings of the Second ACM IKDD Conference on Data Sciences, 2015 Nonnegative matrix factorization (NMF) factorizes a non-negative matrix into product of two non-negative matrices, namely a signal matrix and a mixing matrix. NMF suffers from the scale and ordering ambiguities. Often, the source signals can be monotonous in nature.
Bhatt, Nirav, Ayyar, Arun
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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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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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Smoothed separable nonnegative matrix factorization
Linear Algebra and its Applications, 2023 31 pages + 10 pages of supplementary. Many clarifications have been brought to the paper, and we have added numerical experiments on facial ...
Nicolas Nadisic +2 more
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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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Online kernel nonnegative matrix factorization [PDF]
Signal Processing, 2017 Nonnegative matrix factorization (NMF) has become a prominent signal processing and data analysis technique. To address streaming data, online methods for NMF have been introduced recently, mainly restricted to the linear model. In this paper, we propose a framework for online nonlinear NMF, where the factorization is conducted in a kernel-induced ...
Zhu, Fei, Honeine, Paul
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Nonnegative Matrix Factorization [PDF]
, 2013 Matrix factorization or factor analysis is an important task that is helpful in the analysis of high-dimensional real-world data. SVD is a classical method for matrix factorization, which gives the optimal low-rank approximation to a real-valued matrix in terms of the squared error.
Ke-Lin Du, M. N. S. Swamy
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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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