Simple and effective visual models for gene expression cancer diagnostics
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
An algorithm to cluster data for efficient classification of support vector machines
Expert Systems with Applications: An International Journal
Molecular Diagnosis of Tumor Based on Independent Component Analysis and Support Vector Machines
Computational Intelligence and Security
A division algebraic framework for multidimensional support vector regression
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Motivation: Since DNA microarray experiments provide us with huge amount of gene expression data, they should be analyzed with statistical methods to extract the meanings of experimental results. Some dimensionality reduction methods such as Principal Component Analysis (PCA) are used to roughly visualize the distribution of high dimensional gene expression data. However, in the case of binary classification of gene expression data, PCA does not utilize class information when choosing axes. Thus clearly separable data in the original space may not be so in the reduced space used in PCA. Results: For visualization and class prediction of gene expression data, we have developed a new SVM-based method called multidimensional SVMs, that generate multiple orthogonal axes. This method projects high dimensional data into lower dimensional space to exhibit properties of the data clearly and to visualize a distribution of the data roughly. Furthermore, the multiple axes can be used for class prediction. The basic properties of conventional SVMs are retained in our method: solutions of mathematical programming are sparse, and nonlinear classification is implemented implicitly through the use of kernel functions. The application of our method to the experimentally obtained gene expression datasets for patients' samples indicates that our algorithm is efficient and useful for visualization and class prediction. Contact: komura@hal.rcast.u-tokyo.ac.jp