A Discriminant Analysis for Undersampled Data

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.

• Type: Conference
• Year: 2007
• Where: AUSDM
• Authors: Matthew Robards, Junbin Gao, Philip Charlton
• Keywords: AUSDM 2007, Data Mining, Dimensionality Reduction, Discriminant Analysis, Pattern Recognition