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)
  • Published : 2008.06.18

Abstract

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.

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