DocumentCode
327668
Title
A robust subspace classifier
Author
Bischof, Horst ; Leonardis, Ales ; Pezzei, Florian
Author_Institution
Pattern. Recognition & Image Process. Group, Wien Univ., Austria
Volume
1
fYear
1998
fDate
16-20 Aug 1998
Firstpage
114
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location
Brisbane, Qld.
ISSN
1051-4651
Print_ISBN
0-8186-8512-3
Type
conf
DOI
10.1109/ICPR.1998.711093
Filename
711093
Link To Document