A new perspective to null linear discriminant analysis method and its fast implementation using random matrix multiplication with scatter matrices

DOI10.1016/j.patcog.2011.11.018zbMath1234.62100OpenAlexW2126007985MaRDI QIDQ408033

Alok Sharma, Kuldip K. Paliwal

Publication date: 28 March 2012

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

Full work available at URL: https://doi.org/10.1016/j.patcog.2011.11.018

zbMATH Keywords

dimensionality reductionsmall sample size problemnull LDA

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Random matrices (algebraic aspects) (15B52)

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Uses Software

• UCI-ml

Cites Work

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• Regularized linear discriminant analysis and its application in microarrays
• Matrix multiplication via arithmetic progressions
• A new and fast implementation for null space based linear discriminant analysis
• A generalised K-L expansion method which can deal with small sample size and high-dimensional problems
• A comparison of generalized linear discriminant analysis algorithms
• Expected classification error of the Fisher linear classifier with pseudo-inverse covariance matrix
• Discriminant Analysis with Singular Covariance Matrices: Methods and Applications to Spectroscopic Data
• A theorem on the uncorrelated optimal discriminant vectors
• A direct LDA algorithm for high-dimensional data -- with application to face recognition