Group invariant subspace learning for outlier detection
Bo Fan, Shuchin Aeron · 2016
In this paper, we present a novel method for detecting outliers when the images are misaligned by action of a finite group. Our approach rests on robust learning of group-invariant sub-spaces in presence of outliers. By group-invariant subspaces, we mean subspaces of a vector space that are invariant to action of a finite (Abelian) group. Such scenarios naturally arise in computer vision problems when one is interested in shift (translation) or rotation invariant image processing. While the proposed methods are general, we will focus on misalignment by the group of circular shifts on 2-D images and show that our methods are effective in detecting outliers in real data sets (YaleB and MNIST database) and outperform methods that do not take the group-invariance into account.