Robust Principal Component Analysis with Non-Sparse Errors

• Tác giả: Jushan Bai, Junlong Feng
• Ngôn ngữ: eng
• Ký hiệu phân loại: 521.43 Celestial mechanics
• Thông tin xuất bản: 2019
• Bộ sưu tập: Báo, Tạp chí
• ID: 162645

Tóm tắt

We show that when a high-dimensional data matrix is the sum of a low-rank matrix and a random error matrix with independent entries, the low-rank component can be consistently estimated by solving a convex minimization problem. We develop a new theoretical argument to establish consistency without assuming sparsity or the existence of any moments of the error matrix, so that fat-tailed continuous random errors such as Cauchy are allowed. The results are illustrated by simulations.