A Discriminant Analysis for Undersampled Data

Matthew Robards, Junbin Gao, Philip Charlton

Research output: Book chapter/Published conference paperConference paperpeer-review

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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.
Original languageEnglish
Title of host publicationAIDM2007
EditorsKok-Leong Ong, Wenyuan Li, Junbin Gao
Place of PublicationSydney, Australia
PublisherAustralian Computer Society Inc
Number of pages8
Publication statusPublished - 2007
EventIEEE International Workshop on Integrating AI and Data Mining (AIDM) - Gold Coast, Australia, Australia
Duration: 02 Dec 200706 Dec 2007


WorkshopIEEE International Workshop on Integrating AI and Data Mining (AIDM)


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