A robust subspace classifier

Horst Bischof, Ales Leonardis, Florian Pezzei

Abstract: In this paper we study the problem of missing features and the issues of robustness of subspace classification methods. We propose a new robust method for subspace classification which can cope with missing features and/or outliers. The main idea of our method is to use a robust projection of the patterns onto a subspace. We demonstrate our approach on cervicomotography data and compare our results to the results obtained by using various decision tree algorithms.