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• W2950761275 abstract "We study the Principal Component Analysis (PCA) problem in the distributed and streaming models of computation. Given a matrix $A in R^{m times n},$ a rank parameter $k < rank(A)$, and an accuracy parameter $0 < epsilon < 1$, we want to output an $m times k$ orthonormal matrix $U$ for which $$ || A - U U^T A ||_F^2 le left(1 + epsilon right) cdot || A - A_k||_F^2, $$ where $A_k in R^{m times n}$ is the best rank-$k$ approximation to $A$. This paper provides improved algorithms for distributed PCA and streaming PCA." @default.
• W2950761275 created "2019-06-27" @default.
• W2950761275 creator A5010385482 @default.
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• W2950761275 date "2015-04-25" @default.
• W2950761275 modified "2023-09-25" @default.
• W2950761275 title "Optimal Principal Component Analysis in Distributed and Streaming Models" @default.
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• W2950761275 doi "https://doi.org/10.48550/arxiv.1504.06729" @default.
• W2950761275 hasPublicationYear "2015" @default.
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