Dimension Reduction with Support Vector Regression for Ovarian Cancer Microarray Data

In general, the support vector regression (SVR) is very suitable to approximate a high dimensionality space and ill-posed problem in modeling. That is, the SVR consists of a quadratic programming problem that can be solved efficiently and guaranteed to find a global extremism. Therefore, for the complex data, the SVR is easy to reconstruct an approximated model based on the linear programming technique. On the other hand, a typical microarray data consists of expression levels for a large number of genes on a relatively small number of samples. In order to avoid higher computational complexity and larger prediction errors on high-dimensional problem, we proposed the dimension reduction with SVR for the ovarian cancer microarray data. That is. The SVR can reduce dimension on each sample from 9600 genes to about three hundreds genes. Besides, we can choose the εvalue εvalue εvalue εvalue εvalue εvalue in the loss-function of SVR to obtain the variable number of gene and the proposed method can also overcome the block effect of microarray data. Finally, these results can provide for gene class discovery and gene class prediction.