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Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection
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....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319262/ https://www.ncbi.nlm.nih.gov/pubmed/37409076 http://dx.doi.org/10.7717/peerj-cs.1447 |
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author | Zhang, Zongfu Luo, Qingjia Ying, Zuobin Chen, Rongbin Chen, Hongan |
author_facet | Zhang, Zongfu Luo, Qingjia Ying, Zuobin Chen, Rongbin Chen, Hongan |
author_sort | Zhang, Zongfu |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10319262 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103192622023-07-05 Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection Zhang, Zongfu Luo, Qingjia Ying, Zuobin Chen, Rongbin Chen, Hongan PeerJ Comput Sci Algorithms and Analysis of Algorithms 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. PeerJ Inc. 2023-06-26 /pmc/articles/PMC10319262/ /pubmed/37409076 http://dx.doi.org/10.7717/peerj-cs.1447 Text en © 2023 Zhang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Zhang, Zongfu Luo, Qingjia Ying, Zuobin Chen, Rongbin Chen, Hongan Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection |
title | Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection |
title_full | Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection |
title_fullStr | Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection |
title_full_unstemmed | Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection |
title_short | Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection |
title_sort | design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10319262/ https://www.ncbi.nlm.nih.gov/pubmed/37409076 http://dx.doi.org/10.7717/peerj-cs.1447 |
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