Fisher线性鉴别函数的一种推广形式

A Generalized Form of Fisher Linear Discriminant Function

鉴于常用的两种Fisher鉴别函数在应对奇异问题时存在的不足,给出一种Fisher鉴别函数推广形式,它能将双子空间鉴别分析中两个子空间的鉴别函数统一起来.本文还通过QR分解得到一个正交鉴别向量集,它与Fo-ley-Sammon正交鉴别向量集的鉴别性能很接近,但计算量较小.在2种人脸库上进行实验,实验结果与理论分析一致.

A generalized form of Fisher discriminant function is presented. It overcomes the limitations of two common discriminant functions. The presented form uniforms the discriminant functions in two subspaces of the dual subspace discriminant analysis （DSDA）. A new orthogonal discriminant vector set is obtained by QR decomposition, and its discriminant property is approximate to that of the Foley-Sammon orthogonal discriminant vector set with smaller computational complexity. The experiments on ORL and JAFFE database show that theory analysis is consistent to the experimental results.