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 …

We describe ways to define and calculate L 1-norm signal subspaces that are less sensitive
to outlying data than L 2-calculated subspaces. We start with the computation of the L 1 …

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 …

Cameras usually produce low-quality images under low-light conditions. Though many
methods have been proposed to enhance the visibility of low-light images, they are mainly …

We consider the problem of foreground and background extraction from compressed-
sensed (CS) surveillance videos that are captured by a static CS camera. We propose, for …

Principal component analysis (PCA) based on L1-norm maximization is an emerging
technique that has drawn growing interest in the signal processing and machine learning …

We give the first single-pass streaming algorithm for Column Subset Selection with respect
to the entrywise $\ell_p $-norm with $1\leq p< 2$. We study the $\ell_p $ norm loss since it is …

Standard Principal-Component Analysis (PCA) is known to be sensitive to outliers among
the processed data. On the other hand, L1-norm-based PCA (L1-PCA) exhibits sturdy …

We consider the problem of online foreground extraction from compressed-sensed (CS)
surveillance videos. A technically novel approach is suggested and developed by which the …