Kernel Null Foley-Sammon Transform
Yue Lin, Guochang Gu, Haibo Liu, Jing Shen · 2008
The recently proposed Null Foley–Sammon Transform (NFST) method based on the Gram-Schmidt orthogonalization successfully overcomes the so-called small sample size problem with high performance in terms of recognition accuracy and low computation cost, however, the NFST method is still a linear technique in nature, so a new nonlinear feature extraction method called kernel Null Foley–Sammon transform (KNFST) is presented in this paper. A major advantage of the proposed method is that it is regarded every column of the kernel matrix as a corresponding sample, which is different from other commonly used kernel-based learning algorithms. Then running NFST the in kernel matrix, nonlinear features can be extracted. Experimental results on ORL database indicate that the proposed KNFST method achieves higher recognition rate than the NFST method and other kernel-based learning algorithms.