relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1941/
title: C-KPCA: Custom Kernel PCA for Cancer Classification
creator: Ha, Van Sang
creator: Nguyen, Ha Nam
subject: Information Technology (IT)
description: 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  ...
publisher: Springer International Publishing
date: 2016-07
type: Book Section
type: PeerReviewed
identifier:   Ha, Van Sang and Nguyen, Ha Nam  (2016) C-KPCA: Custom Kernel PCA for Cancer Classification.   In:  Machine Learning and Data Mining in Pattern Recognition.   Springer International Publishing, pp. 459-467.  ISBN 978-3-319-41919-0     
relation: http://link.springer.com/chapter/10.1007/978-3-319-41920-6_36