Linear Discriminant Clustering in Pattern Recognition

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.