Title :
A robust subspace classifier
Author :
Bischof, Horst ; Leonardis, Ales ; Pezzei, Florian
Author_Institution :
Pattern. Recognition & Image Process. Group, Wien Univ., Austria
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
Keywords :
decision trees; pattern classification; cervicomotography data; decision tree algorithms; missing features; robust projection; robust subspace classifier; subspace; Hebbian theory; Laser radar; Least squares methods; Pattern recognition; Principal component analysis; Robustness; Statistical analysis;
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
Print_ISBN :
0-8186-8512-3
DOI :
10.1109/ICPR.1998.711093
