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C-KPCA: Custom Kernel PCA for Cancer Classification

Van Sang Ha and Ha Nam Nguyen (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

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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 ...

Item Type:Book Section
Subjects:Information Technology (IT)
Divisions:Faculty of Information Technology (FIT)
ID Code:1941
Deposited By: Dr Hà Nam Nguyễn
Deposited On:28 Nov 2016 02:33
Last Modified:28 Nov 2016 02:33

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