Optimal vs. classical linear dimension reduction

Michael C. Röhl and Claus Weihs

No 1998,12, Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen

Abstract: We describe a computer intensive method for linear dimension reduction which minimizes the classification error directly. Simulated annealing Bohachevsky et al (1986) is used to solve this problem. The classification error is determined by an exact integration. We avoid distance or scatter measures which are only surrogates to circumvent the classification error. Simulations in two dimensions and analytical approximations demonstrate the superiority of optimal classification opposite to the classical procedures. We compare our procedure to the well-known canonical discriminant analysis (homoscedastic case) as described in Mc Lachlan (1992) and to a method by Young et al (1986) for the heteroscedastic case. Special emphasis is put on the case when the distance based methods collapse. The computer intensive algorithm always achieves minimal classification error.

Date: 1998
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.econstor.eu/bitstream/10419/77322/2/1998-12.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb475:199812

Access Statistics for this paper

More papers in Technical Reports from Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().