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

Author:

Zhang Zongfu1,Luo Qingjia2,Ying Zuobin2,Chen Rongbin1,Chen Hongan1

Affiliation:

1. College of Information Engineering, Jiangmen Polytechnic, Jiangmen, China

2. Faculty of Data Science, City University of Macau, Macau, China

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.

Funder

Macau Foundation under its Research Fund

Jiangmen Basic and Applied Research’s Main Project for 2022

Key Project of Jiangmen Basic and Applied Basic Research in 2022

Guang-Dong Science and Technology Innovation Strategy Fund

Publisher

PeerJ

Subject

General Computer Science

Reference35 articles.

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