Linear discriminant analysis (LDA) has been widely used to extract features to solve various
machine learning problems. However, the current LDA methods can be confused by highly
dispersed samples. To address this problem, we develop a new LDA method based on a
new expression of the theoretical error in classification. The method first combines different
class distributions and then constructs the projection matrix on the basis of the statistics of
the combined distribution. This is very different from conventional methods that estimate the …

Linear discriminant analysis (LDA) has been widely used to extract features to solve various
machine learning problems. However, the current LDA methods can be confused by highly
dispersed samples. To address this problem, we develop a new LDA method based on a
new expression of the theoretical error in classification. The method first combines different
class distributions and then constructs the projection matrix on the basis of the statistics of
the combined distribution. This is very different from conventional methods that estimate the …