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• W192680930 abstract "Nonnegative Matrix Factorization (NMF) is a dimensionality reduction method for representing nonnegative data in a low-dimensional nonnegative space. NMF problems are usually solved with an alternating minimization of a given objective function, using nonnegativity constrained optimization algorithms. This paper is concerned with the projected trust-region algorithm that is adapted to minimize a family of divergences or statistical distances, such as α- or β-divergences that are efficient for solving NMF problems. Using the Cauchy point estimate for the quadratic approximation model, a radius of the trust-region can be estimated efficiently for a symmetric and block-diagonal structure of the corresponding Hessian matrices. The experiments demonstrate a high efficiency of the proposed approach." @default.
• W192680930 created "2016-06-24" @default.
• W192680930 creator A5022656662 @default.
• W192680930 date "2012-01-01" @default.
• W192680930 modified "2023-09-26" @default.
• W192680930 title "Trust-Region Algorithm for Nonnegative Matrix Factorization with Alpha- and Beta-divergences" @default.
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• W192680930 doi "https://doi.org/10.1007/978-3-642-32717-9_23" @default.
• W192680930 hasPublicationYear "2012" @default.
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