Using sparse learning for feature selection with locality structure preserving based on positive data
Zahir Noorie, Fatemeh Afsari · 2018
Feature selection aims to select the most relevant subset of features to make classifiers more accurate, fast, and easy to understand. Regularized sparse feature selection methods using L1-norm regularization term in their optimization problem, have received much attention in recent years. In this paper, besides the L1-norm regularization term a locality structure preserving regularization term based on positive data points is proposed. Positive data is defined as the unique data points involved in the must-link constraints, which are the most informative data. The experimental results on a number of datasets, show the efficacy of the proposed method for classification tasks compared with similar feature selection methods.