Work

FS-GSR: Graph-based scoring and sparse reconstruction for group streaming feature selection

Year: 2026

Type: article

Abstract: High-dimensional data often presents the core challenge of features arriving dynamically in grouped streams. This paper proposes a novel feature selection algorithm named Feature Selection with Group Streaming via Graph-based Scoring and Sparse Reconstruction (FS-GSR). The method establishes a three-stage framework comprising graph-guided hybrid filtering, intra-group structure preservation, and inter-group sparse reconstruction. This framework effectively leverages both the geometric structure and discriminative information of the data, ensuring high classification performance while significantly eliminating feature redundancy. Numerical experiments on multiple real-world datasets demonstrate that FS-GSR outperforms existing state-of-the-art methods in terms of both classification accuracy and feature compression capability, exhibiting superior overall performance and robustness.

Source: Applied Intelligence

DOI: 10.1007/s10489-026-07498-2

Author: Tianyuan Jia

Institution: Hong Kong Polytechnic University

Language: English

Topic: Face and Expression Recognition

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

Funder: Hong Kong Polytechnic University