Nonparametric discriminant analysis and nearest neighbor classification

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.