An unconstrained optimal set of discriminant vectors
Zeng Xian-gui, LI Shao-fa, Wenming Zuo · 2005
Under circumstances of orthogonal constraints, the vectors that make the Fisher discriminant criterion function attain the maximum values are F-S optimal set of discriminant vectors. In this paper, an optimal set of discriminant vectors which need not fill any constraint condition has been presented, together with the solution for the set. In addition, when the number of training samples is smaller than the dimensions of training samples (i.e. small number of training samples problem), the within-class scatter matrix is singular. Under this circumstance, to acquire both F-S optimal set of discriminant vectors and unconstrained Optimal Set of Discriminant Vectors presented here becomes unfeasible. To solve this problem, an approvedFishcr discriminant function is presented. The result of experiment on OFU face database show that the algorithms prescntcd here have strong ability in discrimination.