COMPREHENSIVE ANALYSIS FOR THE LOCAL FISHER DISCRIMINANT ANALYSIS
Junbin Gao, Paul Kwan, Xiaodi Huang · International Journal of Pattern Recognition and Artificial Intelligence · 2009
Using local data information, the recently proposed local Fisher Discriminant Analysis (LFDA) algorithm18 provides a new way of handling the multimodal issues within classes where the conventional Fisher Discriminant Analysis (FDA) algorithm fails. Like the FDA algorithm (global counterpart), the LFDA suffers when it is applied to the higher dimensional data sets. In this paper, we propose a new formulation by which a robust algorithm can be formed. The new algorithm offers more robust results for higher dimensional data sets when compared with the LFDA in most cases. By extensive simulation studies, we have demonstrated the practical usefulness and robustness of our new algorithm in data visualization.