Weighted maximum variance dimensionality reduction

Turki Turki, Usman Roshan

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

3 Scopus citations

Abstract

Dimensionality reduction procedures such as principal component analysis and the maximum margin criterion discriminant are special cases of a weighted maximum variance (WMV) approach. We present a simple two parameter version of WMV that we call 2P-WMV. We study the classification error given by the 1-nearest neighbor algorithm on features extracted by our and other dimensionality reduction methods on several real datasets. Our results show that our method yields the lowest average error across the datasets with statistical significance.

Original languageEnglish (US)
Title of host publicationPattern Recognition - 6th Mexican Conference, MCPR 2014, Proceedings
PublisherSpringer Verlag
Pages11-20
Number of pages10
ISBN (Print)9783319074900
DOIs
StatePublished - 2014
Event6th Mexican Conference on Pattern Recognition, MCPR 2014 - Cancun, Mexico
Duration: Jun 25 2014Jun 28 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8495 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other6th Mexican Conference on Pattern Recognition, MCPR 2014
CountryMexico
CityCancun
Period6/25/146/28/14

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • dimensionality reduction
  • maximum margin criterion
  • principal component analysis

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