Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy
نویسندگان
چکیده
منابع مشابه
Graph Regularized Non-negative Matrix Factorization By Maximizing Correntropy
Non-negative matrix factorization (NMF) has proved effective in many clustering and classification tasks. The classic ways to measure the errors between the original and the reconstructed matrix are l2 distance or KullbackLeibler (KL) divergence. However, nonlinear cases are not properly handled when we use these error measures. As a consequence, alternative measures based on nonlinear kernels,...
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Nonnegative matrix factorization (NMF) has been successfully applied to many areas for classification and clustering. Commonly-used NMF algorithms mainly target on minimizing the l2 distance or Kullback-Leibler (KL) divergence, which may not be suitable for nonlinear case. In this paper, we propose a new decomposition method by maximizing the correntropy between the original and the product of ...
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Data involving repeated measurements of several variables over different factors, experimental conditions or time may exhibit correlations among variables, as well as between factors. The discovery of these underlying, meaningful relations is important to a wide variety of areas such as psychology, signal processing, finance, among others. Common methods such as independent component analysis, ...
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Nonnegative matrix factorization (NMF) aims to decompose a given data matrix X into the product of two lower-rank nonnegative factor matrices UV T . Graph regularized NMF (GNMF) is a recently proposed NMF method that preserves the geometric structure of X during such decomposition. Although GNMF has been widely used in computer vision and data mining, its multiplicative update rule (MUR) based ...
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Nonnegative matrix factorization (NMF) methods have proved to be powerful across a wide range of real-world clustering applications. Integrating multiple types of measurements for the same objects/subjects allows us to gain a deeper understanding of the data and refine the clustering. We have developed a novel Graph-reguarized multiview NMF-based method for data integration called EquiNMF. The ...
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ژورنال
عنوان ژورنال: Journal of Computers
سال: 2014
ISSN: 1796-203X
DOI: 10.4304/jcp.9.11.2570-2579