Using minimum classification error training in dimensionality reduction
Xiangdong Wang, K.K. Paliwal · 2002
Dimensionality reduction is an important problem in pattern recognition. In a speech recognition system, the size of the feature set is normally large in the order of 40. Therefore, it is necessary to reduce the dimensionality of the feature space for efficient and effective speech recognition. Two popular methods to reduce the dimensionality of the feature space are linear discriminat analysis (LDA) and principal component analysis (PCA). This paper uses the minimum error classification (MCE) training algorithm for dimensionality reduction and presents an alternative MCE training algorithm that performs better on testing data than the conventional MCE training algorithm. The effects of the initial value of the transformation matrix on the performance of MCE have also been studied.