Improve Handwritten Character Recognition Performance by Heteroscedastic Linear Discriminant Analysis
Hailong Liu, Xiaoqing Ding · 2006
In this paper, we propose a new linear dimensionality reduction method to deal with heteroscedastic feature distribution in handwritten character recognition. Marc Loog’s between-class scatter matrix decomposition and directed distance matrix (DDM) concept is adopted, while the Chernoff criterion he used is replaced by a new Mahalanobis criterion proposed in this paper, and the pairwiseclass calculation is removed to reduce computational cost. We experiment our heteroscedastic linear discriminant analysis algorithm on different character recognition problems, and demonstrate its superiority over conventional linear discriminant analysis.