TY  - CHAP
ID  - SisLab1941
UR  - http://link.springer.com/chapter/10.1007/978-3-319-41920-6_36
A1  - Ha, Van Sang
A1  - Nguyen, Ha Nam
Y1  - 2016/07//
N2  - 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  ...
PB  - Springer International Publishing
SN  - 978-3-319-41919-0
TI  - C-KPCA: Custom Kernel PCA for Cancer Classification
SP  - 459
AV  - none
EP  - 467
T2  - Machine Learning and Data Mining in Pattern Recognition
ER  -