A feature selection method based on kernel canonical correlation analysis and the minimum Redundancy–Maximum Relevance filter method

In this paper, we propose a feature selection method based on a recently popular minimum Redundancy–Maximum Relevance (mRMR) criterion, which we called Kernel Canonical Correlation Analysis based mRMR (KCCAmRMR) based on the idea of finding the unique information, i.e. information that is distinct from the set of already selected variables, that a candidate variable possesses about the target variable. In simplest terms, for this purpose, we propose using correlated functions explored by KCCA instead of using the features themselves as inputs to mRMR. We demonstrate the usefulness of our method on both toy and benchmark datasets.