Boutsidis, Christos; Woodruff, David P.; Zhong, Peilin Optimal principal component analysis in distributed and streaming models. (English) Zbl 1381.62140 Wichs, Daniel (ed.) et al., Proceedings of the 48th annual ACM SIGACT symposium on theory of computing, STOC ’16, Cambridge, MA, USA, June 19–21, 2016. New York, NY: Association for Computing Machinery (ACM) (ISBN 978-1-4503-4132-5). 236-249 (2016). Cited in 10 Documents MSC: 62H25 Factor analysis and principal components; correspondence analysis 15A23 Factorization of matrices 65F25 Orthogonalization in numerical linear algebra 65F50 Computational methods for sparse matrices 68Q05 Models of computation (Turing machines, etc.) (MSC2010) 68Q10 Modes of computation (nondeterministic, parallel, interactive, probabilistic, etc.) 68Q17 Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.) 68W15 Distributed algorithms Keywords:column subset selection; distributed model of computation; low-rank matrix decomposition; lower bounds; principal component analysis; singular value decomposition; sparse matrix; streaming model of computation Citations:Zbl 1304.65138 PDFBibTeX XMLCite \textit{C. Boutsidis} et al., in: Proceedings of the 48th annual ACM SIGACT symposium on theory of computing, STOC '16, Cambridge, MA, USA, June 19--21, 2016. New York, NY: Association for Computing Machinery (ACM). 236--249 (2016; Zbl 1381.62140) Full Text: DOI arXiv