FisherLDA.h File Reference

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Classes
class	CFisherLDA
	Preprocessor FisherLDA attempts to model the difference between the classes of data by performing linear discriminant analysis on input feature vectors/matrices. When the init method in FisherLDA is called with proper feature matrix X(say N number of vectors and D feature dimensions) supplied via apply_to_feature_matrix or apply_to_feature_vector methods, this creates a transformation whose outputs are the reduced T-Dimensional & class-specific distribution (where T<= number of unique classes-1). The transformation matrix is essentially a DxT matrix, the columns of which correspond to the specified number of eigenvectors which maximizes the ratio of between class matrix to within class matrix. More...

Enumerations
enum	EFLDAMethod { AUTO_FLDA = 10, CANVAR_FLDA = 20, CLASSIC_FLDA = 30 }