NPDA/CS: Improved Non-parametric Discriminant Analysis with CS decomposition and its application to face recognition
Qingsong Zeng, Changdong Wang · 2010
Fisher's Linear Discriminant Analysis (FLDA) uses the parametric form of the scatter matrix which is based on the Gaussian distribution assumption, and requires the scatter matrices to be nonsingular, which can not always be satisfied. To overcome this problem, many scholars have recently proposed Non-parametric Discriminant Analysis (NPDA), addressing the non-Gaussian aspects of sample distributions. In this paper, from the nearest neighborhood perspective, a new formulation of scatter matrices is presented to improve the NPDA, simultaneously emphasizing the boundary information and local structure contained in the training set. Then, CS decomposition is incorporated to improve its performance. Experimental results on 4 databases demonstrate the effectiveness of the improved method.