Boosting performance for 2D Linear Discriminant Analysis via regression

Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called Regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an ex

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Nam Nguyen, Wanquan Liu, Svetha Venkatesh
• Keywords: Computer Vision, ICPR 2008, Linear Discriminant Analysis, Pairwise Distances, Sample Size Problem