A Discriminant Analysis for Undersampled Data

Matthew Robards, Junbin Gao, Philip Charlton

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

4 Downloads (Pure)

Abstract

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
Pages11-18
Number of pages8
Volume84
Publication statusPublished - 2007
EventIEEE International Workshop on Integrating AI and Data Mining (AIDM) - Gold Coast, Australia, Australia
Duration: 02 Dec 200706 Dec 2007

Workshop

WorkshopIEEE International Workshop on Integrating AI and Data Mining (AIDM)
CountryAustralia
Period02/12/0706/12/07

Fingerprint Dive into the research topics of 'A Discriminant Analysis for Undersampled Data'. Together they form a unique fingerprint.

Cite this