Mixture subclass discriminant analysis
In this letter, mixture subclass discriminant analysis (MSDA) that alleviates two shortcomings of subclass discriminant analysis (SDA) is proposed. In particular, it is shown that for data with Gaussian homoscedastic subclass structure a) SDA does not guarantee to provide the discriminant subspace that minimizes the Bayes error, and, b) the sample covariance matrix can not be used as the minimization metric of the discriminant analysis stability criterion (DSC). Based on this analysis MSDA modifies the objective function of SDA and utilizes a novel partitioning procedure to aid discrimination of data with Gaussian homoscedastic subclass structure. Experimental results confirm the improved classification performance of MSDA.