eprintid: 1941
rev_number: 6
eprint_status: archive
userid: 300
dir: disk0/00/00/19/41
datestamp: 2016-11-28 02:33:02
lastmod: 2016-11-28 02:33:02
status_changed: 2016-11-28 02:33:02
type: book_section
metadata_visibility: show
creators_name: Ha, Van Sang
creators_name: Nguyen, Ha Nam
creators_id: namnh@vnu.edu.vn
title: C-KPCA: Custom Kernel PCA for Cancer Classification
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: 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  ...
date: 2016-07
date_type: published
publisher: Springer International Publishing
official_url: http://link.springer.com/chapter/10.1007/978-3-319-41920-6_36
full_text_status: none
pagerange: 459-467
refereed: TRUE
isbn: 978-3-319-41919-0
book_title: Machine Learning and Data Mining in Pattern Recognition
citation:   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