Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection

PeerJ Comput Sci. 2023 Jun 26:9:e1447. doi: 10.7717/peerj-cs.1447. eCollection 2023.

Abstract

High dimension and complexity of network high-dimensional data lead to poor feature selection effect network high-dimensional data. To effectively solve this problem, feature selection algorithms for high-dimensional network data based on supervised discriminant projection (SDP) have been designed. The sparse representation problem of high-dimensional network data is transformed into an Lp norm optimization problem, and the sparse subspace clustering method is used to cluster high-dimensional network data. Dimensionless processing is carried out for the clustering processing results. Based on the linear projection matrix and the best transformation matrix, the dimensionless processing results are reduced by combining the SDP. The sparse constraint method is used to achieve feature selection of high-dimensional data in the network, and the relevant feature selection results are obtained. The experimental findings demonstrate that the suggested algorithm can effectively cluster seven different types of data and converges when the number of iterations approaches 24. The F1 value, recall, and precision are all kept at high levels. High-dimensional network data feature selection accuracy on average is 96.9%, and feature selection time on average is 65.1 milliseconds. The selection effect for network high-dimensional data features is good.

Keywords: Feature selection; Network high-dimensional data; Sparse constraint; Sparse subspace clustering; Supervised discriminant projection.

Grants and funding

This research is supported by the Macau Foundation under its Research Fund (Grant No. MF2102), Macau; the Jiangmen Basic and Applied Research’s main project for 2022, “Research on virtual reality multi-person collaborative interaction and efficient rendering technology for intelligent manufacturing under 5G environment” (project No. JZ202216); the “Research and development of 5G CPE based on 5G core technologies and” end-to-end cloud “architecture” key project of Jiangmen basic and applied basic research in 2022 (project No. JZ202215); and the Guang-dong Science and Technology Innovation Strategy Fund (“Climbing Prcject” in 2021) Project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.