Robust principal component analysis (RPCA) is a critical tool in modern machine learning,
which detects outliers in the task of low-rank matrix reconstruction. In this paper, we propose …

This paper considers the use of robust principal component analysis (RPCA) in a CUR
decomposition framework and applications thereof. Our main algorithms produce a robust …

Spatiotemporal traffic data imputation is of great significance in intelligent transportation
systems and data-driven decision-making processes. To perform efficient learning and …

Robust principal component analysis (RPCA) is a widely used tool for dimension reduction.
In this work, we propose a novel non-convex algorithm, coined Iterated Robust CUR …

Exponential function is a basic form of temporal signals, and how to fast acquire this signal is
one of the fundamental problems and frontiers in signal processing. To achieve this goal …

We study the problem of tensor robust principal component analysis (TRPCA), that aims to
separate an underlying low-multilinear-rank tensor and a sparse outlier tensor from their …

We study the tensor robust principal component analysis (TRPCA) problem, a tensorial
extension of matrix robust principal component analysis, which aims to split the given tensor …

This paper develops a novel sparse direction-of-arrival (DOA) estimation technique that
avoids the common requirement of hyperparameters, which are typically difficult to set …

This paper presents gridless sparse processing for direction-of-arrival (DOA) estimation. The
method solves a gridless version of sparse covariance-based estimation using alternating …

Abstract 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 …