Pattern classification using eigenspace projection

Chen Ta Hsieh, Chin Chuan Han, Chang Hsing Lee, Kou Chin Fan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Covariance matrices play the key role for dimension reduction in eigenspace projection methods for pattern recognition. Two scatters, an intraclass scatter and an interclass scatter, are obtained from samples for describing the sample distributions. The representation for these two scatters is classified into four categories. In this study, we focus on the analysis of the intraclass and interclass scatters. Three experiments, the evaluation for a music genre dataset, a bird sound dataset, and four face datasets, are conducted to make the comparisons of several state-of-the-art algorithms.

Original languageEnglish
Title of host publicationProceedings of the 2012 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2012
Pages154-157
Number of pages4
DOIs
StatePublished - 2012
Event2012 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2012 - Piraeus-Athens, Greece
Duration: 18 Jul 201220 Jul 2012

Publication series

NameProceedings of the 2012 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2012

Conference

Conference2012 8th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2012
Country/TerritoryGreece
CityPiraeus-Athens
Period18/07/1220/07/12

Keywords

  • Covariance matrix
  • global mean-based scatter
  • local mean-based scatter
  • pairwise point-based scatter
  • point-to-space based scatter

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