A practical application of kernel-based fuzzy discriminant analysis - Gao, Jian-Qiang; Fan, Li-Ya; Li, Li; Xu, Li-Zhong (Zielona Góra: Uniwersytet Zielonogórski)
A novel method for feature extraction and recognition called Kernel Fuzzy Discriminant Analysis (KFDA) is proposed in this paper to deal with recognition problems, e.g., for images. The KFDA method is obtained by combining the advantages of fuzzy methods and a kernel trick. Based on the orthogonal-triangular decomposition of a matrix and Singular Value Decomposition (SVD), two different variants, KFDA/QR and KFDA/SVD, of KFDA are obtained.
Keywords: kernel fuzzy discriminant analysis; fuzzy k-nearest neighbor; QR decomposition; SVD; fuzzy membership matrix; t-test