Improved discriminate analysis for high-dimensional data and its application to face recognition

DOI10.1016/j.patcog.2006.11.015zbMath1113.68086OpenAlexW2111149694MaRDI QIDQ869053

Xiao-Sheng Zhuang, Dao-Qing Dai

Publication date: 26 February 2007

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2006.11.015

zbMATH Keywords

feature extractionprincipal component analysisface recognitionlinear discriminant analysissmall sample size problem

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (6)

A study on three linear discriminant analysis based methods in small sample size problem ⋮ A general framework for dimensionality reduction of K-means clustering ⋮ A practical application of kernel-based fuzzy discriminant analysis ⋮ Feature extraction based on Laplacian bidirectional maximum margin criterion ⋮ Face recognition using discriminant locality preserving projections based on maximum margin criterion ⋮ Incremental learning of bidirectional principal components for face recognition

Uses Software

• KPCA plus LDA

Cites Work

• An extensive comparison of recent classification tools applied to microarray data
• Regularized discriminant analysis and its application to face recognition
• A shape- and texture-based enhanced Fisher classifier for face recognition
• Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data
• A direct LDA algorithm for high-dimensional data -- with application to face recognition
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