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IJPRAI
2006

Nearest Neighbor Discriminant Analysis

13 years 3 months ago
Nearest Neighbor Discriminant Analysis
Linear Discriminant Analysis (LDA) is a popular feature extraction technique in statistical pattern recognition. However, it often suffers from the small sample size problem when dealing with the high dimensional data. Moreover, while LDA is guaranteed to find the best directions when each class has a Gaussian density with a common covariance matrix, it can fail if the class densities are more general. In this paper, a new nonparametric feature extraction method, stepwise nearest neighbor discriminant analysis(SNNDA), is proposed from the point of view of the nearest neighbor classification. SNNDA finds the important discriminant directions without assuming the class densities belong to any particular parametric family. It does not depend on the nonsingularity of the within-class scatter matrix either. Our experimental results demonstrate that SNNDA outperforms the existing variant LDA methods and the other stateof-art face recognition approaches on three datasets from ATT and FERET f...
Xipeng Qiu, Lide Wu
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where IJPRAI
Authors Xipeng Qiu, Lide Wu
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