Unsupervised Feature Selection Via Data Reconstruction and Side Information

Abstract

Data reconstruction, which aims at preserving statistical properties of the data during the reconstruction has become a new criterion for feature selection. Although feature selection could benefit from the perspective of data reconstruction, it is unable to exploit other crucial information, namely, graph structure and pairwise constraints. To address previously mentioned deficiency, we propose a novel feature selection approach in this paper, known as unsupervised feature selection via data reconstruction and side information . More specifically, the proposed method takes advantage of the prior knowledge regarding pairwise constraints (side information), the minimization of data reconstruction error, and the graph embedding simultaneously, such that pivotal features are selected with preserving data manifold structure. To obtain the robust solution, a robust loss function is applied to the feature selection problem, which interpolates between $\ell _{1}$ -norm and $\ell _{2}$ -norm. Eventually, extensive experiments are conducted to demonstrate the effectiveness of the proposed method.