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 language | English |
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Title of host publication | AIDM2007 |
Editors | Kok-Leong Ong, Wenyuan Li, Junbin Gao |
Place of Publication | Sydney, Australia |
Publisher | Australian Computer Society Inc |
Pages | 11-18 |
Number of pages | 8 |
Volume | 84 |
Publication status | Published - 2007 |
Event | IEEE International Workshop on Integrating AI and Data Mining (AIDM) - Gold Coast, Australia, Australia Duration: 02 Dec 2007 → 06 Dec 2007 |
Workshop
Workshop | IEEE International Workshop on Integrating AI and Data Mining (AIDM) |
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Country/Territory | Australia |
Period | 02/12/07 → 06/12/07 |