Semi-supervised feature selection under logistic I-RELIEF framework

We consider feature selection in the semi-supervised learning setting. This problem is rarely addressed in the literature. We propose a new algorithm as a natural extension of the recently developed Logistic I-RELIEF algorithm. The basic idea of the proposed algorithm is to modify the objective function of Logistic I-RELIEF to include the margins of unlabeled samples by following the large margin principle. Experimental results on artificial and benchmark datasets are presented to demonstrate the viability of the newly proposed method.