  In this paper, we present a novel embedded feature selection method based on
a Multi-layer Perceptron (MLP) network and generalize it for group-feature or
sensor selection problems, which can control the level of redundancy among the
selected features or groups. Additionally, we have generalized the group lasso
penalty for feature selection to encompass a mechanism for selecting valuable
group features while simultaneously maintaining a control over redundancy. We
establish the monotonicity and convergence of the proposed algorithm, with a
smoothed version of the penalty terms, under suitable assumptions. Experimental
results on several benchmark datasets demonstrate the promising performance of
the proposed methodology for both feature selection and group feature selection
over some state-of-the-art methods.
