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OJB©
Online Journal of Bioinformatics©
Volume 6
(1):91-98, 2005.
Shen Li,
Tan EC.
School of Computer
Engineering, Nanyang Technological University, Singapore, 639798.
ABSTRACT
Li S, Tan EC., Combining Kernel Dimension-Reduction with Regularized Classifiers for
Tissue Categorization, Onl J Bioinform., 6
(1):91-98, 2005. Microarrays can be used for accurate cancer
diagnosis using machine learning techniques. However, the number of samples
(arrays) are significantly smaller than the number of variables (genes)
invalidating traditional statistical methods. The application
of dimension reduction methods have achieved good performance. In this
paper, two kernel dimension-reduction methods, KPLS and KPCA are considered.
After dimension reduction, a linear RLSC is followed up for classification.
Thus two classifiers, KPLS+RLSC and KPCA+RLSC have been derived and tested on
several cancer datasets. It has been illustrated that by using mutual
orthogonality of the components from kernel dimension reduction, a very simple
solution can be derived for the linear RLSC, which only requires the inversion
of a sparse and low-dimensional matrix. The two algorithms have also been shown
to have excellent performance in comparison with SVM. Several other issues
regarding the usage of KPLS and KPCA are discussed and could be useful when
combining them with other classification methods.
Key words: kernel, dimension, classifiers, tissue.