Stepwise nearest neighbor discriminant analysis

Authors:
Xipeng Qiu;Lide Wu
Affiliations:
Media Computing & Web Intelligence Lab, Department of Computer Science and Engineering, Fudan University, Shanghai, China;Media Computing & Web Intelligence Lab, Department of Computer Science and Engineering, Fudan University, Shanghai, China
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

Citing 6
Cited 5

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Discriminant Adaptive Nearest Neighbor Classification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric discriminant analysis and nearest neighbor classification

Pattern Recognition Letters
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Letters: Two-dimensional nearest neighbor discriminant analysis

Neurocomputing
Optimal dimensionality of metric space for classification

Proceedings of the 24th international conference on Machine learning
Metric learning by discriminant neighborhood embedding

Pattern Recognition
Robust linearly optimized discriminant analysis

Neurocomputing
Terrorist organization behavior prediction algorithm based on context subspace

ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 state-of-art face recognition approaches on three datasets from ATT and FERET face databases.