Abstract:

Feature selection, which aims to reduce data’s dimensionality by removing redundant features, is one of the main issues in the field of machine learning. Most of existing graphbased semisupervised feature selection algorithms are suffering from neglecting clear cluster structure. We propose a semisupervised algorithm based on l1norm graph in this paper. A joint learning framework is built upon cluster structure and feature selection; l1-norm is imposed to guarantee the sparsity of the cluster structure, which is suitable for feature selection. To select the most relevant features and reduce the effect of outliers, the l2，1-norm regularization is added into the objective function. We evaluate the performance of the proposed algorithm over several data sets and compare the results with state-of-the-art semi-supervised feature selection algorithms. The results demonstrate the effectiveness of the proposed algorithm.

Key words: feature selection;semi-supervised learning;l2, 1-norm;l1-norm