Efficient classification scheme based on hybrid global and local properties of feature

Heesung Lee, Sungjun Hong, Sungje An, Euntai Kim

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

2 Citations (Scopus)

Abstract

This paper proposes a new pattern classification scheme, combining global and local features. The proposed method uses principal component analysis (PCA) for global property and locality preserving projections (LPP) for local property of the pattern. PCA is known for preserving the most descriptive ones after projection while LPP is known for preserving the neighborhood structure of the data set. Our combing method integrates global and local descriptive information and finds a richer set of alternatives beyond PCA and LPP in a 2-D parametric space. In order to find the hybrid features adaptively and find optimal parameters, we employ the genetic algorithm (GA). Experiments are performed with UCI machine learning repository to show the performance of the proposed algorithm.

Original languageEnglish
Title of host publication2008 International Conference on Control, Automation and Systems, ICCAS 2008
Pages2126-2129
Number of pages4
DOIs
Publication statusPublished - 2008
Event2008 International Conference on Control, Automation and Systems, ICCAS 2008 - Seoul, Korea, Republic of
Duration: 2008 Oct 142008 Oct 17

Publication series

Name2008 International Conference on Control, Automation and Systems, ICCAS 2008

Other

Other2008 International Conference on Control, Automation and Systems, ICCAS 2008
CountryKorea, Republic of
CitySeoul
Period08/10/1408/10/17

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering

Fingerprint Dive into the research topics of 'Efficient classification scheme based on hybrid global and local properties of feature'. Together they form a unique fingerprint.

Cite this