Perturbation LDA: Learning the difference between the class empirical mean and its expectation

DOI10.1016/J.PATCOG.2008.09.012zbMath1178.68523OpenAlexW2154889509MaRDI QIDQ1010082

Pong C. Yuen, Wei-Shi Zheng, Stan Z. Li, Jian Huang Lai

Publication date: 3 April 2009

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2008.09.012

zbMATH Keywords

perturbation analysisface recognitionFisher criterion

Mathematics Subject Classification ID

Pattern recognition, speech recognition (68T10)

Related Items (1)

Optimal subset-division based discrimination and its kernelization for face and palmprint recognition

Uses Software

• FERET
• CMU PIE

Cites Work

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• Regularized discriminant analysis and its application to face recognition
• Two-dimensional discriminant transform for face recognition
• 1D-LDA vs. 2D-LDA: When is vector-based linear discriminant analysis better than matrix-based?
• Face recognition using direct, weighted linear discriminant analysis and modular subspaces
• An optimal transformation for discriminant and principal component analysis
• Discriminant Functions when Covariances are Unequal and Sample Sizes are Moderate
• Regularized Gaussian Discriminant Analysis Through Eigenvalue Decomposition
• THE STATISTICAL UTILIZATION OF MULTIPLE MEASUREMENTS
• Face recognition based on the uncorrelated discriminant transformation
• A direct LDA algorithm for high-dimensional data -- with application to face recognition

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