On the Power of Truncated SVD for General High-rank Matrix Estimation Problems

Mar 22 (Wednesday) at 2pm GHC-8102

Speaker: Yining Wang

Abstract: We show that given an estimate $\widehat{A}$ that is close to a general high-rank positive semi-definite (PSD) matrix $\widehat{A}$ in spectral norm, the simple truncated SVD of Ab produces a multiplicative approximation of A in Frobenius norm. This observation leads to many interesting results on general high rank matrix estimation problems, including high rank matrix completion, high rank matrix denoising and low-rank estimation of high-dimensional covariance. Link: https://arxiv.org/abs/1702.06861