%A Van Sang Ha
%A Ha Nam Nguyen
%T C-KPCA: Custom Kernel PCA for Cancer Classification
%X Abstract Principal component analysis (PCA) is an effective and well-known method for reducing high-dimensional data sets. Recently, KPCA (Kernel PCA), a nonlinear form of PCA, has been introduced into many fields. In this paper, we propose a new gene selection, namely Custom Kernel principal component analysis (C-KPCA). The new kernel function for KPCA is created by combining a set of kernel functions. First, Singular Value Decomposition (SVD) is used to reduce the dimension of microarray data. Input space is then mapped to  ...
%P 459-467
%B Machine Learning and Data Mining in Pattern Recognition
%D 2016
%I Springer International Publishing
%L SisLab1941