Adaptive Classification Algorithm Based on Maximum Scatter Difference Discriminant Criterion

In this paper we first prove that the optimal discriminant direction of Maximum scatter difference(MSD)discriminant criterion with a certain value co is equivalent to the optimal Fisher discriminant direction.Second,sample recognition rate curves of MSD are illustrated.The recognition rate curve is usually a pulse curve when the within-class scatter matrix is nonsingnlar.With the increase of parameter C,the recognition rate of MSD also increases.The recognition rate of MSD achieves its maximum when C is equal to c_0.In addition,former study showed that,when the within-class scatter matrix is singular,MSD criterion is approaching the large margin linear projection criterion as parameter C increases. Moreover,the recognition rate curve of MSD is non-decreasing.Thus,an adaptive classifica- tion algorithm based on maximum scatter difference discriminant criterion is proposed based on these facts.The new algorithm can tune parameter C automatically according to the characteristics of training samples.Experiment conducted on 6 datasets from UCI Machine Learning Repository and AR face database demonstrates that the adaptive classification algorithm for maximum scatter difference has good classification property.