Abstract
Nonparametric discriminant analysis (NDA), opposite to other nonparametric techniques, has received little or no attention within the pattern recognition community. Nearest neighbor classification (NN) instead, has a well established position among other classification techniques due to its practical and theoretical properties. In this paper, we observe that when we seek a linear representation adapted to improve NN performance, what we obtain not surprisingly is quite close to NDA. Since a hierarchy is provided on the extracted features it also serves as a dimensionality reduction technique that preserves NN performance. Experiments evaluate and compare NN classification using our proposed representation against more classical feature extraction techniques. © 2003 Elsevier B.V. All rights reserved.
Original language | English |
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Pages (from-to) | 2743-2749 |
Journal | Pattern Recognition Letters |
Volume | 24 |
Issue number | 15 |
DOIs | |
Publication status | Published - 1 Jan 2003 |
Keywords
- Face recognition
- Nearest neighbors classifier
- Nonparametric discriminant analysis