Feature extraction based on difference vectors

Taeuk Jeong, Jong Geun Park, Chulhee Lee

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

1 Citation (Scopus)

Abstract

In a typical classification procedure of high dimensional data, feature extraction is first applied to reduce the dimensionality and a classifier is employed. However, in most feature extraction methods, covariance matrices must be estimated. When training samples are limited, this estimation is inherently biased, thereby generating ineffective features. In this paper, we propose a new feature extraction method for high dimensional hyperspectral data when limited training samples are available. In the proposed method, we construct a feature matrix using available training samples. The proposed method calculates the difference vector feature matrix using weighted difference vectors among the training samples. Experimental results show that the proposed method improves classification accuracy even if the size of training sample is very small.

Original languageEnglish
Title of host publication2nd IEEE International Workshop on Soft Computing Applications Proceedings, SOFA 2007
Pages183-186
Number of pages4
DOIs
Publication statusPublished - 2007
Event2nd IEEE International Workshop on Soft Computing Applications, SOFA 2007 - Oradea, Romania
Duration: 2007 Aug 212007 Aug 23

Publication series

NameSOFA 2007 - 2nd IEEE International Workshop on Soft Computing Applications Proceedings

Other

Other2nd IEEE International Workshop on Soft Computing Applications, SOFA 2007
CountryRomania
CityOradea
Period07/8/2107/8/23

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

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