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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Official URL: http://link.springer.com/chapter/10.1007/978-3-319...
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)|
|Deposited By:||Dr Hà Nam Nguyễn|
|Deposited On:||28 Nov 2016 02:33|
|Last Modified:||28 Nov 2016 02:33|
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