Maximizing Correlation for Supervised Classification

In this paper, we develop a novel feature selection and classification approach using the correlation maximization paradigm. This approach is particularly interesting when the number of features is very large in comparison to the number of samples, as in the datasets arising in the bioinformatics applications. We illustrate our method by showing 100 genetic markers which act together in separating ovarian endometroid tumors from other ovarian epithelial tumors. Notice that in the previous works, there was no single marker gene found for this purpose.