Global Structure Preservation and Self-Representation-Based Supervised Feature Selection

Abstract

Feature selection aims to select a subset of features from high-dimensional data, which can overcome the curse of dimensionality for the next dealing steps. However, the feature selection itself could face the curse of dimensionality. To overcome the above problem, in this paper, a new feature selection framework is designed according to a human processing in our daily life. In our daily life, to evaluate a candidate's ability to work, the related professional knowledge and the comprehensive ability of a candidate should be both evaluated. Actually, a candidate only with good professional knowledge often hardly solves new problems in the work. Based on the above analysis, in our new designed framework, the features are selected by evaluating its ability of global structure preservation and self-representation, which are respectively similar to the professional knowledge and comprehensive ability in evaluating candidate. As a result, the selected features can accommodate larger changes in test data. The conducted experiments validate the effectiveness of our feature selection.