Title: Informed Non-convex Robust Principal Component Analysis with Features,
Abstract: We revisit the problem of robust principal component analysis with features
acting as prior side information. To this aim, a novel, elegant, non-convex
optimization approach is proposed to decompose a given observation matrix into
a low-rank core and the corresponding sparse residual. Rigorous theoretical
analysis of the proposed algorithm results in exact recovery guarantees with
low computational complexity. Aptly designed synthetic experiments demonstrate
that our method is the first to wholly harness the power of non-convexity over
convexity in terms of both recoverability and speed. That is, the proposed
non-convex approach is more accurate and faster compared to the best available
algorithms for the problem under study. Two real-world applications, namely
image classification and face denoising further exemplify the practical
superiority of the proposed method.