In current graph embedding methods, low dimensional projections are obtained by
preserving either global geometrical structure of data or local geometrical structure of data …

Recently, the robustification of principal component analysis (PCA) has attracted much
research attention in numerous areas of science and engineering. The most popular and …

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 …

The inductive matrix completion (IMC) problem is to recover a low rank matrix from few
observed entries while incorporating prior knowledge about its row and column subspaces …

Objective: Highlights always occur in endoscopic images due to their special imaging
environment. It not only increases the difficulty of observation and diagnosis from surgeons …

Robust principal component analysis (RPCA) is widely studied in computer vision. Recently
an adaptive rank estimate based RPCA has achieved top performance in low-level vision …

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 …

This paper proposes a unified Superposed Atomic Representation (SAR) framework for high-
dimensional data recovery with multiple low-dimensional structures. The data can be in …

This paper proposes an effective and robust method for image alignment and recovery on a
set of linearly correlated data via Frobenius and L2, 1 norms. The most popular and …