Randomized Algorithms for Low-Rank Matrix Factorizations: Sharp Performance Bounds
نویسندگان
چکیده
منابع مشابه
RANDOMIZED ALGORITHMS FOR LOW-RANK FACTORIZATIONS: SHARP PERFORMANCE BOUNDS By
The development of randomized algorithms for numerical linear algebra, e.g. for computing approximate QR and SVD factorizations, has recently become an intense area of research. This paper studies one of the most frequently discussed algorithms in the literature for dimensionality reduction—specifically for approximating an input matrix with a low-rank element. We introduce a novel and rather i...
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ژورنال
عنوان ژورنال: Algorithmica
سال: 2014
ISSN: 0178-4617,1432-0541
DOI: 10.1007/s00453-014-9891-7