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Proceedings Paper

Supervised linear dimensionality reduction with robust margins for object recognition
Author(s): F. Dornaika; A. Assoum
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Paper Abstract

Linear Dimensionality Reduction (LDR) techniques have been increasingly important in computer vision and pattern recognition since they permit a relatively simple mapping of data onto a lower dimensional subspace, leading to simple and computationally efficient classification strategies. Recently, many linear discriminant methods have been developed in order to reduce the dimensionality of visual data and to enhance the discrimination between different groups or classes. Many existing linear embedding techniques relied on the use of local margins in order to get a good discrimination performance. However, dealing with outliers and within-class diversity has not been addressed by margin-based embedding method. In this paper, we explored the use of different margin-based linear embedding methods. More precisely, we propose to use the concepts of Median miss and Median hit for building robust margin-based criteria. Based on such margins, we seek the projection directions (linear embedding) such that the sum of local margins is maximized. Our proposed approach has been applied to the problem of appearance-based face recognition. Experiments performed on four public face databases show that the proposed approach can give better generalization performance than the classic Average Neighborhood Margin Maximization (ANMM). Moreover, thanks to the use of robust margins, the proposed method down-grades gracefully when label outliers contaminate the training data set. In particular, we show that the concept of Median hit was crucial in order to get robust performance in the presence of outliers.

Paper Details

Date Published: 4 February 2013
PDF: 10 pages
Proc. SPIE 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, 86620R (4 February 2013); doi: 10.1117/12.2003791
Show Author Affiliations
F. Dornaika, Univ. of the Basque Country (Spain)
IKERBASQUE, Basque Foundation for Science (Spain)
A. Assoum, Lebanese Univ. (Lebanon)

Published in SPIE Proceedings Vol. 8662:
Intelligent Robots and Computer Vision XXX: Algorithms and Techniques
Juha Röning; David Casasent, Editor(s)

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