@incollection{SisLab1941,
       booktitle = {Machine Learning and Data Mining in Pattern Recognition},
           month = {July},
           title = {C-KPCA: Custom Kernel PCA for Cancer Classification},
          author = {Van Sang Ha and Ha Nam Nguyen},
       publisher = {Springer International Publishing},
            year = {2016},
           pages = {459--467},
             url = {https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1941/},
        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  ...}
}