Linear discriminant analysis (Linear?) Discriminant? Analysis,? LDA), sometimes also called Fisher linear discriminant (Fisher?) Linear? DISCRIMINANT?,FLD),? Is this algorithm Ronald? Fisher, invented in the 1936, is a classic algorithm for pattern recognition. In the 1996, the field of pattern recognition and artificial intelligence was introduced by Belhumeur.
The basic idea is to project the high-dimensional pattern sample to the best discriminant vector space, in order to achieve the effect of extracting the classified information and compressing the spatial dimension of the feature, and then ensuring that the model sample has the maximal inter-class distance and the smallest intra-class distance in the new subspace, that is, the model has the best separable in the space. Therefore, it is an effective feature extraction method. By using this method, it is possible to maximize the scattering matrix between classes of the projected pattern samples and minimize the dispersion matrix in the class. That is, it can guarantee that the pattern sample in the new space has the smallest intra-class distance and the largest inter-class distance, that is, the pattern has the best separable in the space.
Linear discriminant method in descending dimension algorithm LDA
