Improve Heteroscedastic Discriminant Analysis by Using CBP Algorithm

Linear discriminant analysis is considered as current techniques in feature extraction so, LDA, by discriminant information which obtains in mapping space, does the classification act. When the classes' distribution is not normal, LDA, to perform classification, will face problem and will resulted the poor performance of criteria in performing the classification act. One of the proposed ways is the use of other measures, such as Chernoff's distance so, by using Chernoff's measure LDA has been spreading to its heterogeneous states and LDA in this state, in addition to use information among the medians, uses the information of the classes' Covariance matrices. By defining scattering matrix, based on Boundary and non- Boundary samples and using these matrices in Chernoff's criteria, the decrease of the classes' overlapping in the mapping space in as result, the rate of classification correctness increases. Using Boundary and non- Boundary samples in scattering matrices causes improvement over the result. In this article, we use a new discovering multi-stage Algorithm to choose Boundary and non- Boundary samples so, the results of the conducted experiments shows promising performance of the proposing method.