We study the Principal Component Analysis (PCA) problem in the distributed
and streaming models of computation. Given a matrix AβRmΓn, a
rank parameter k<rank(A), and an accuracy parameter 0<Ο΅<1, we
want to output an mΓk orthonormal matrix U for which β£β£AβUUTAβ£β£F2ββ€(1+Ο΅)β β£β£AβAkββ£β£F2β, where AkββRmΓn is the best rank-k approximation to A.
This paper provides improved algorithms for distributed PCA and streaming
PCA.Comment: STOC2016 full versio