An Alternating Rank-<i>k</i> Nonnegative Least Squares Framework (ARkNLS) for Nonnegative Matrix Factorization
نویسندگان
چکیده
An Alternating Rank-k Nonnegative Least Squares Framework (ARkNLS) for Matrix Factorization
منابع مشابه
A Projected Alternating Least square Approach for Computation of Nonnegative Matrix Factorization
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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In this paper we consider the Nonnegative Matrix Factorization (NMF) problem: given an (elementwise) nonnegative matrix V ∈ R + find, for assigned k, nonnegative matrices W ∈ R + and H ∈ R k×n + such that V = WH . Exact, non trivial, nonnegative factorizations do not always exist, hence it is interesting to pose the approximate NMF problem. The criterion which is commonly employed is I-divergen...
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ژورنال
عنوان ژورنال: SIAM Journal on Matrix Analysis and Applications
سال: 2021
ISSN: ['1095-7162', '0895-4798']
DOI: https://doi.org/10.1137/20m1352405