This paper proposes an incremental solution to Fast Subclass Discriminant Analysis (fastSDA). We present an exact and an approximate linear solution, along with an approximate kernelized variant. Extensive experiments on eight image datasets with different incremental batch sizes show the superiority of the proposed approach in terms of training time and accuracy being equal or close to fastSDA solution and outperforming other methods.
We propose to test for the homogeneity of two samples by using Kernel Fisher discriminant Analysis. This provides us with a consistent nonparametric test statistic, for which we derive the asymptotic distribution under the null hypothesis. We give experimental evidence of the relevance of our method on both artificial and real datasets. Papers published at the Neural Information Processing Systems Conference.
Dichotomy transformation in biometric authentication problem creates a two class (""within"" or ""between"") classification problem in multivariate distance space. Linear discriminant analysis, which is a linear classifier, results in good performance in IRIS biometric authentication problem. However, it assumes that the distributions of two classes are normal, whereas they are closely related to the log-normal distributions. Here a modified variance linear discriminant analysis algorithm is proposed and its superior experimental results on the IRIS biometric database are reported.