clMF: A fine-grained and portable alternating least squares algorithm for parallel matrix factorization
نویسندگان
چکیده
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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A sparse QR-factorization algorithm SPARQR for coarse-grained parallel computations is described. The coeecient matrix, which is assumed to be general sparse, is reordered in an attempt to bring as many zero elements in the lower left corner as possible. The reordered matrix is then partitioned into block rows, and Givens plane rotations are applied in each block-row. These are independent task...
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Nonnegative Matrix Factorization (NMF) is a popular technique in a variety of fields due to its component-based representation with physical interpretablity. NMF finds a nonnegative hidden structures as oblique bases and coefficients. Recently, Orthogonal NMF (ONMF), imposing an orthogonal constraint into NMF, has been gathering a great deal of attention. ONMF is more appropriate for the cluste...
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This paper presents a new fine-grained parallel algorithm for computing an incomplete LU factorization. All nonzeros in the incomplete factors can be computed in parallel and asynchronously, using one or more sweeps that iteratively improve the accuracy of the factorization. Unlike existing parallel algorithms, the new algorithm does not depend on reordering the matrix. Numerical tests show tha...
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ژورنال
عنوان ژورنال: Future Generation Computer Systems
سال: 2020
ISSN: 0167-739X
DOI: 10.1016/j.future.2018.04.071