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Tutorial: Complexity analysis of Singular Value Decomposition and its variants

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arxiv 1906.12085 v3 pith:X6A5T5B5 submitted 2019-06-28 math.NA cs.LGcs.NAstat.ML

Xiaocan Li , Shuo Wang , Yinghao Cai This is my paper · ORCID
classification math.NAcs.LGcs.NAstat.ML
keywords analysiscomparedcomplexitydecompositionkrylovmethodrandomizedsingular
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We compared the regular Singular Value Decomposition (SVD), truncated SVD, Krylov method and Randomized PCA, in terms of time and space complexity. It is well-known that Krylov method and Randomized PCA only performs well when k << n, i.e. the number of eigenpair needed is far less than that of matrix size. We compared them for calculating all the eigenpairs. We also discussed the relationship between Principal Component Analysis and SVD.

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Cited by 3 Pith papers

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