Riemannian CUR Decompositions for Robust Principal Component Analysis
This work addresses Robust PCA for data analysis applications, offering incremental improvements in outlier tolerance and computational efficiency.
The paper tackles the problem of Robust PCA by proposing RieCUR, a nonconvex algorithm that recovers low-rank and sparse matrices from their sum, achieving state-of-the-art performance with comparable computational complexity to existing methods and improved robustness to outliers.
Robust Principal Component Analysis (PCA) has received massive attention in recent years. It aims to recover a low-rank matrix and a sparse matrix from their sum. This paper proposes a novel nonconvex Robust PCA algorithm, coined Riemannian CUR (RieCUR), which utilizes the ideas of Riemannian optimization and robust CUR decompositions. This algorithm has the same computational complexity as Iterated Robust CUR, which is currently state-of-the-art, but is more robust to outliers. RieCUR is also able to tolerate a significant amount of outliers, and is comparable to Accelerated Alternating Projections, which has high outlier tolerance but worse computational complexity than the proposed method. Thus, the proposed algorithm achieves state-of-the-art performance on Robust PCA both in terms of computational complexity and outlier tolerance.