We study the Principal Component Analysis (PCA) problem in the distributed
and streaming models of computation. Given a matrix A∈Rm×n, a
rank parameter k<rank(A), and an accuracy parameter 0<ϵ<1, we
want to output an m×k orthonormal matrix U for which ∣∣A−UUTA∣∣F2​≤(1+ϵ)⋅∣∣A−Ak​∣∣F2​, where Ak​∈Rm×n is the best rank-k approximation to A.
This paper provides improved algorithms for distributed PCA and streaming
PCA.Comment: STOC2016 full versio