We study the convergence rate of gradient-based local search methods for solving low-rank
matrix recovery problems with general objectives in both symmetric and asymmetric cases …

Robust low-rank matrix completion (RMC), or robust principal component analysis with
partially observed data, has been studied extensively for computer vision, signal processing …

This work addresses the robust principal component analysis (PCA) problem using
generalized nonoconvex regularization for low-rank and sparsity promotion. While the …

Recovering a low-rank matrix and a sparse matrix from an observed matrix, known as
sparse and low-rank decomposition (SLRD), is becoming a hot topic in recent years. The …

Accurately pursuing network traffic anomalies is crucial to network maintenance and
management. However, existing methods generally focus on detecting uniformly distributed …

We study the convergence rate of gradient-based local search methods for solving low-rank
matrix recovery problems with general objectives in both symmetric and asymmetric cases …

Many key problems in machine learning and data science are routinely modeled as
optimization problems and solved via optimization algorithms. With the increase of the …