Video semantic recognition usually suffers from the curse of dimensionality and the absence
of enough high-quality labeled instances, thus semisupervised feature selection gains …

Recently, there are many works on discriminant analysis, which promote the robustness of
models against outliers by using L 1-or L 2, 1-norm as the distance metric. However, both of …

Dimensionality reduction techniques extract low-dimensional structure from high-
dimensional data and are widespread in machine learning research. In practice, due to …

Principal component analysis (PCA) is widely used in dimensionality reduction. A lot of
variants of PCA have been proposed to improve the robustness of the algorithm. However …

We propose a generative model for robust tensor factorization in the presence of both
missing data and outliers. The objective is to explicitly infer the underlying low …

Of late, there are many studies on the robust discriminant analysis, which adopt L 1-norm as
the distance metric, but their results are not robust enough to gain universal acceptance. To …

Principal component analysis (PCA) is one of the most widely used multivariate techniques
in statistics. It is commonly used to reduce the dimensionality of data in order to examine its …

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 …

This paper presents a remarkably simple, yet powerful, algorithm termed coherence pursuit
(CoP) to robust principal component analysis (PCA). As inliers lie in a low-dimensional …