A study on three linear discriminant analysis based methods in small sample size problem

DOI10.1016/j.patcog.2007.06.001zbMath1119.68180OpenAlexW2104834378MaRDI QIDQ2384958

Jun Liu, Xiaoyang Tan, Song-can Chen

Publication date: 10 October 2007

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2007.06.001

zbMATH Keywords

eigenvalue distributionsmall sample sizediscriminant common vectorsRegularized discriminant analysismaximal margin criterion

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (5)

A Rayleigh-Ritz style method for large-scale discriminant analysis ⋮ Laplacian pair-weight vector projection for semi-supervised learning ⋮ Extracting the optimal dimensionality for local tensor discriminant analysis ⋮ A regularization framework for robust dimensionality reduction with applications to image reconstruction and feature extraction ⋮ A Median Nearest Neighbors LDA for Anomaly Network Detection

Uses Software

• rda
• AR face
• COIL-20

Cites Work

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• Subspace evolution analysis for face representation and recognition
• Improved discriminate analysis for high-dimensional data and its application to face recognition
• Linear dimensionality reduction using relevance weighted LDA
• Generalized null space uncorrelated Fisher discriminant analysis for linear dimensionality reduction
• Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
• Chaos control using least-squares support vector machines
• Efficient quadratic regularization for expression arrays

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