Using discriminant analysis for multi-class classification

Tao Li, Shenghuo Zhu, Mitsunori Ogihara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

27 Citations (Scopus)

Abstract

Discriminant analysis is known to learn discriminative feature transformations. This paper studies its use in multi-class classification problems. The performance is tested on a large collection of benchmark datasets.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
Pages589-592
Number of pages4
StatePublished - 2003
Externally publishedYes
Event3rd IEEE International Conference on Data Mining, ICDM '03 - Melbourne, FL, United States
Duration: Nov 19 2003Nov 22 2003

Other

Other3rd IEEE International Conference on Data Mining, ICDM '03
CountryUnited States
CityMelbourne, FL
Period11/19/0311/22/03

Fingerprint

Discriminant analysis

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Li, T., Zhu, S., & Ogihara, M. (2003). Using discriminant analysis for multi-class classification. In Proceedings - IEEE International Conference on Data Mining, ICDM (pp. 589-592)

Using discriminant analysis for multi-class classification. / Li, Tao; Zhu, Shenghuo; Ogihara, Mitsunori.

Proceedings - IEEE International Conference on Data Mining, ICDM. 2003. p. 589-592.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Li, T, Zhu, S & Ogihara, M 2003, Using discriminant analysis for multi-class classification. in Proceedings - IEEE International Conference on Data Mining, ICDM. pp. 589-592, 3rd IEEE International Conference on Data Mining, ICDM '03, Melbourne, FL, United States, 11/19/03.
Li T, Zhu S, Ogihara M. Using discriminant analysis for multi-class classification. In Proceedings - IEEE International Conference on Data Mining, ICDM. 2003. p. 589-592
Li, Tao ; Zhu, Shenghuo ; Ogihara, Mitsunori. / Using discriminant analysis for multi-class classification. Proceedings - IEEE International Conference on Data Mining, ICDM. 2003. pp. 589-592
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