Nonlinear kernel MSE methods for cancer classification

Authors:
L. Shen;E. C. Tan
Affiliations:
School of Computer Engineering, Nanyang Technological University, Singapore, Singapore;School of Computer Engineering, Nanyang Technological University, Singapore, Singapore
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

Combination of kernel PLS (KPLS) and kernel SVD (KSVD) with minimum-squared-error (MSE) criteria has created new machine learning methods for cancer classification and has been successfully applied to seven publicly available cancer datasets. Besides the high accuracy of the new methods, very fast training speed is also obtained because the matrix inversion in the original MSE procedure is avoided. Although the KPLS-MSE and the KSVD-MSE methods have equivalent accuracies, the KPLS achieves the same results using significantly less but more qualitative components.