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• W2795393485 startingPage "4409" @default.
• W2795393485 abstract "An efficient, accurate and reliable approximation of a matrix by one of lower rank is a fundamental task in numerical linear algebra and signal processing applications. In this paper, we introduce a new matrix decomposition approach termed Subspace-Orbit Randomized singular value decomposition (SOR-SVD), which makes use of random sampling techniques to give an approximation to a low-rank matrix. Given a large and dense data matrix of size $mtimes n$ with numerical rank $k$, where $k ll text{min} {m,n}$, the algorithm requires a few passes through data, and can be computed in $O(mnk)$ floating-point operations. Moreover, the SOR-SVD algorithm can utilize advanced computer architectures, and, as a result, it can be optimized for maximum efficiency. The SOR-SVD algorithm is simple, accurate, and provably correct, and outperforms previously reported techniques in terms of accuracy and efficiency. Our numerical experiments support these claims." @default.
• W2795393485 created "2018-04-13" @default.
• W2795393485 creator A5039334577 @default.
• W2795393485 creator A5049028312 @default.
• W2795393485 date "2018-08-15" @default.
• W2795393485 modified "2023-09-26" @default.
• W2795393485 title "Subspace-Orbit Randomized Decomposition for Low-Rank Matrix Approximations" @default.
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• W2795393485 doi "https://doi.org/10.1109/tsp.2018.2853137" @default.
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