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Fisher linear discrimination analysis has become one of the most effective way to feature extraction, but in the case of high dimension and small sample how to extract Fisher optimal identification features is still a difficult, still hasn t completely solve the problem. In this paper, introducing the idea of compression mapping and isomorphism, ingeniously solved the high-dimensional theoretically, singular case to solve the problem of optimal identification vector set, and the whole process of the method to solve the optimal identification of vector set just in a low dimensional transformation space, compared with the traditional method greatly reduces the amount of calculation. Based on this theory, further to high dimension and small sample situation of optimal discrimination analysis method to establish the framework, a generic algorithm is first as K L transform, reoccupy Fisher identification transformations as a secondary feature extraction. Based on this algorithm framework, a