Daming Shi

Title: Introduction to Robust Principal Component Analysis

Abstract:

Principal component analysis (PCA) is a widely-used method for
dimension reduction, classification, clustering and
regression. However, the traditional PCA is sensitive to outliers
and/or noise. In this talk, robust principal component analysis (RPCA)
will be introduced as a subspace approach. RPCA recovers a low-rank
data component from the superposition of a sparse component. The
augmented Lagrange multiplier (ALM) method enjoys the highest accuracy
among all the approaches to the RPCA. However, it still suffers from
two problems, namely, a brutal force initialisation phase resulting in
low convergence speed and ignorance of other types of noise resulting
in low accuracy. To this end, this talk will also further convert the
RPCA to a dual problem.