대한전자공학회:학술대회논문집 (Proceedings of the IEEK Conference)

대한전자공학회 (The Institute of Electronics and Information Engineers)
권호 표지

Linear Discriminant Clustering in Pattern Recognition

  • Sun, Zhaojia (Department of Computer Science and Engineening Chungnam National University) ;
  • Choi, Mi-Seon (Human Resource Development Consortium for Next Generation Software in Information Technology, Chungnam National University) ;
  • Kim, Young-Kuk (Department of Computer Science and Engineening Chungnam National University)
  • 발행 : 2008.06.18
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Fisher Linear Discriminant(FLD) is a sample and intuitive linear feature extraction method in pattern recognition. But in some special cases, such as un-separable case, one class data dispersed into several clustering case, FLD doesn't work well. In this paper, a new discriminant named K-means Fisher Linear Discriminant, which combines FLD with K-means clustering is proposed. It could deal with this case efficiently, not only possess FLD's global-view merit, but also K-means' local-view property. Finally, the simulation results also demonstrate its advantage against K-means and FLD individually.

키워드