This paper will serve as an introduction to the body of work on robust subspace recovery.
Robust subspace recovery involves finding an underlying low-dimensional subspace in a …

This review article gives a high-level overview of the approaches across different scales of
organization and levels of abstraction. The studies covered in this paper include …

It was shown recently that the K L1-norm principal components (L1-PCs) of a real-valued
data matrix X∈ RD× N (N data samples of D dimensions) can be exactly calculated with cost …

We study the ℓ1-low rank approximation problem, where for a given nxd matrix A and
approximation factor α≤ 1, the goal is to output a rank-k matrix  for which‖ A-Â‖ 1≤ α …

Recently, many ℓ 1-norm-based PCA approaches have been developed to improve the
robustness of PCA. However, most existing approaches solve the optimal projection matrix …

As a popular feature extraction algorithm, the 2D local preserving projections (2DLPP)
algorithm has been successfully applied in many fields. Using 2D image representation, the …

Recently, the annihilating filter-based low-rank Hankel matrix (ALOHA) approach was
proposed as a powerful image inpainting method. Based on the observation that …

Singular spectrum analysis (SSA), starting from the second half of the 20th century, has
been a rapidly develo** method of time series analysis. Since it can be called principal …

Recently, L1-norm-based discriminant subspace learning has attracted much more attention
in dimensionality reduction and machine learning. However, most existing approaches solve …

Principal component analysis (PCA) along with its extensions to manifolds and outlier
contaminated data have been indispensable in computer vision and machine learning. In …