Results 71 to 80 of about 1,627,028 (236)
Mathematics, 2023 NONNEGATIVE matrix factorization (NMF) is an effective technique for dimensionality reduction of high-dimensional data for tasks such as machine learning and data visualization.
Jie Li, Yaotang Li, Chaoqian Li
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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 ...
Nadisic, Nicolas +2 more
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Compact Manifolds With Unbounded Nilpotent Fundamental Groups and Positive Ricci Curvature
Communications on Pure and Applied Mathematics, EarlyView.ABSTRACT It follows from the work of Kapovitch and Wilking that a closed manifold with nonnegative Ricci curvature has a uniformly almost nilpotent fundamental group. Leftover questions and conjectures, have asked if in this context the fundamental group is actually uniformly almost abelian. The main goal of this work is to construct examples (Mk9,gk)$(
Elia Bruè, Aaron Naber, Daniele Semola
wiley +1 more source
Probabilistic Non-Negative Matrix Factorization with Binary Components
Mathematics, 2021 Non-negative matrix factorization is used to find a basic matrix and a weight matrix to approximate the non-negative matrix. It has proven to be a powerful low-rank decomposition technique for non-negative multivariate data.
Xindi Ma +4 more
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A multilevel approach for nonnegative matrix factorization [PDF]
Journal of Computational and Applied Mathematics, 2012 Nonnegative Matrix Factorization (NMF) is the problem of approximating a nonnegative matrix with the product of two low-rank nonnegative matrices and has been shown to be particularly useful in many applications, e.g., in text mining, image processing, computational biology, etc. In this paper, we explain how algorithms for NMF can be embedded into the
Nicolas Gillis, François Glineur
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Categorical Dimensions of Human Odor Descriptor Space Revealed by Non-Negative Matrix Factorization [PDF]
, 2013 In contrast to most other sensory modalities, the basic perceptual dimensions of olfaction remain unclear. Here, we use non-negative matrix factorization (NMF) – a dimensionality reduction technique – to uncover structure in a panel of odor profiles ...
Castro, Jason B. +16 more
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Exchange Rates and Sovereign Risk: A Nonlinear Approach Based on Local Gaussian Correlations
International Journal of Finance &Economics, EarlyView.ABSTRACT We empirically assess the interlinkages between sovereign risk, measured in terms of CDS spreads, and exchange rates for a sample of emerging markets. Our period of analysis includes episodes of severe stress, such as the Global Financial Crisis, the COVID‐19 pandemic, and the Ukrainian War.
Reinhold Heinlein +2 more
wiley +1 more source
Diversified Bayesian Nonnegative Matrix Factorization
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Nonnegative matrix factorization (NMF) has been widely employed in a variety of scenarios due to its capability of inducing semantic part-based representation. However, because of the non-convexity of its objective, the factorization is generally not unique and may inaccurately discover intrinsic “parts” from the data.
Maoying Qiao +4 more
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Convex and Semi-Nonnegative Matrix Factorizations [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010 We present several new variations on the theme of nonnegative matrix factorization (NMF). Considering factorizations of the form X=FG(T), we focus on algorithms in which G is restricted to containing nonnegative entries, but allowing the data matrix X to have mixed signs, thus extending the applicable range of NMF methods.
Chris H. Q. Ding +2 more
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On the effect of the perturbation of a nonnegative matrix on its Perron eigenvector [PDF]
, 1982 Elsner L, Johnson CR, Neumann MM. On the effect of the perturbation of a nonnegative matrix on its Perron eigenvector. Czechoslovak Mathematical Journal.
Elsner, Ludwig F. +5 more
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