Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method
نویسندگان
چکیده
منابع مشابه
Nonnegative Matrix Factorization Based on Alternating Nonnegativity Constrained Least Squares and Active Set Method
The non-negative matrix factorization (NMF) determines a lower rank approximation of a matrix where an interger "!$# is given and nonnegativity is imposed on all components of the factors % & (' and % )'* ( . The NMF has attracted much attention for over a decade and has been successfully applied to numerous data analysis problems. In applications where the components of the data are necessaril...
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Nonnegative matrix factorization (NMF) is a common method in data mining that have been used in different applications as a dimension reduction, classification or clustering method. Methods in alternating least square (ALS) approach usually used to solve this non-convex minimization problem. At each step of ALS algorithms two convex least square problems should be solved, which causes high com...
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Abstract. We consider a new kind of low rank matrix approximation problem for nonnegative matrices: given a nonnegative matrix M , approximate it with a low rank product V.H such that V.H is nonnegative, but without nonnegativity constraints on V and H separately. The nonnegativity constraint on V.H is natural when using the Kullback-Leibler divergence as optimality criterion. We propose an ite...
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ژورنال
عنوان ژورنال: SIAM Journal on Matrix Analysis and Applications
سال: 2008
ISSN: 0895-4798,1095-7162
DOI: 10.1137/07069239x