Reviews: Kernel Feature Selection via Conditional Covariance Minimization

In this paper, authors propose a new nonlinear feature selection based on kernels. More specifically, the conditional covariance operator has been employed to measure the conditional independence between Y and X given the subset of X. Then, the feature selection can be done by searching a set of features that minimizing the conditional independence. This optimization problem results in minimizing over matrix inverse and it is hard to optimize it. Thus, a novel approach to deal with the matrix inverse problem is also proposed.