Subspace-Orbit Randomized Decomposition for Low-Rank Matrix Approximations
نویسندگان
چکیده
منابع مشابه
Subspace-Orbit Randomized Decomposition for Low-rank Matrix Approximation
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 (SORSVD), which makes use of random sampling techniques to give an approximation to a low-rank matrix....
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ژورنال
عنوان ژورنال: IEEE Transactions on Signal Processing
سال: 2018
ISSN: 1053-587X,1941-0476
DOI: 10.1109/tsp.2018.2853137