Accelerating Nonnegative Matrix Factorization Algorithms Using Extrapolation
نویسندگان
چکیده
منابع مشابه
Nonnegative Matrix Factorization: Algorithms and Parallelization
An alternative to singular value decomposition (SVD) in the information retrieval is the low-rank approximation of an original non-negative matrix A by its non-negative factors U and V . The columns of U are the feature vectors with no non-negative components, and the columns of V store the non-negative weights that serve for the combination of feature vectors. First experiments show that restr...
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The nonnegative matrix factorization (NMF) is a boundconstrained low-rank approximation technique for nonnegative multivariate data. NMF has been studied extensively over the last years, but an important aspect which only has received little attention so far is a proper initialization of the NMF factors in order to achieve a faster error reduction. Since the NMF objective function is usually no...
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Nonnegative matrix factorization (NMF) has been successfully applied in di erent elds, such as text mining, image processing, and video analysis. NMF is the problem of determining two nonnegative low rank matrices U and V , for a given input matrix M , such that M ≈ UV >. There is an increasing interest in parallel and distributed NMF algorithms, due to the high cost of centralized NMF on large...
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Approximate nonnegative matrix factorization is an emerging technique with a wide spectrum of potential applications in data analysis. Currently, the most-used algorithms for this problem are those proposed by Lee and Seung [7]. In this paper we present a variation of one of the Lee-Seung algorithms with a notably improved performance. We also show that algorithms of this type do not necessaril...
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ژورنال
عنوان ژورنال: Neural Computation
سال: 2019
ISSN: 0899-7667,1530-888X
DOI: 10.1162/neco_a_01157