A Discriminant Analysis for Undersampled Data

Robards, M., Gao, J. and Charlton, P.

One of the inherent problems in pattern recognition is the undersampled data problem, also known as the curse of dimensionality reduction. In this paper a new algorithm called pairwise discriminant analysis (PDA) is proposed for pattern recognition. PDA, like linear discriminant analysis (LDA), performs dimensionality reduction and clustering, without suffering from undersampled data to the same extent as LDA.

Cite as: Robards, M., Gao, J. and Charlton, P. (2007). A Discriminant Analysis for Undersampled Data. In Proc. 2nd International Workshop on Integrating Artificial Intelligence and Data Mining (AIDM 2007), Gold Coast, Queensland, Australia. CRPIT, 84. Ong, K.-L., Li, W. and Gao, J., Eds. ACS. 11-18.

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