Unsupervised Feature Selection Algorithm Based on Graph Filtering and Self-representation

TL;DR

This paper introduces an unsupervised feature selection method that leverages graph filtering and self-representation to better capture the intrinsic data structure, improving robustness and discriminative feature selection.

Contribution

It proposes a novel unsupervised feature selection algorithm combining higher-order graph filtering with self-representation and an iterative solution approach.

Findings

Effective in capturing intrinsic data structure

Enhances robustness through l2,1 norm regularization

Verified by simulation experiments

Abstract

Aiming at the problem that existing methods could not fully capture the intrinsic structure of data without considering the higher-order neighborhood information of the data, we proposed an unsupervised feature selection algorithm based on graph filtering and self-representation. Firstly,a higher-order graph filter was applied to the data to obtain its smooth representation,and a regularizer was designed to combine the higher-order graph information for the self-representation matrix learning to capture the intrinsic structure of the data. Secondly,l2,1 norm was used to reconstruct the error term and feature selection matrix to enhance the robustness and row sparsity of the model to select the discriminant features. Finally, an iterative algorithm was applied to effectively solve the proposed objective function and simulation experiments were carried out to verify the effectiveness of the proposed algorithm.