Robust Structured Low-Rank Approximation on the Grassmannian

no code implementations • 12 Jun 2015 • Clemens Hage, Martin Kleinsteuber

Over the past years Robust PCA has been established as a standard tool for reliable low-rank approximation of matrices in the presence of outliers.

Time Series Time Series Forecasting

pROST : A Smoothed Lp-norm Robust Online Subspace Tracking Method for Realtime Background Subtraction in Video

no code implementations • 8 Feb 2013 • Florian Seidel, Clemens Hage, Martin Kleinsteuber

An increasing number of methods for background subtraction use Robust PCA to identify sparse foreground objects.

Robust PCA and subspace tracking from incomplete observations using L0-surrogates

no code implementations • 2 Oct 2012 • Clemens Hage, Martin Kleinsteuber

Many applications in data analysis rely on the decomposition of a data matrix into a low-rank and a sparse component.

Outlier Detection