9 citations · 9 across the 1 of their papers we have counts for
2 papers
math.NA2020
Fast algorithms for robust principal component analysis with an upper bound on the rank
Ningyu Sha, Lei Shi, Ming Yan
The robust principal component analysis (RPCA) decomposes a data matrix into a low-rank part and a sparse part. There are mainly two types of algorithms for RPCA. The first type of…
cs.LG2019★ 9 cited
Manifold Denoising by Nonlinear Robust Principal Component Analysis
He Lyu, Ningyu Sha, Shuyang Qin +3
This paper extends robust principal component analysis (RPCA) to nonlinear manifolds. Suppose that the observed data matrix is the sum of a sparse component and a component drawn f…