Improved Ramp-Based Twin Support Vector Clustering
Twin support vector clustering based on Hinge loss and twin support vector clustering based on Ramp loss are two new twin support vector clustering algorithms, which provide a new research idea for solving the clustering problem, and gradually become a research hotspot in pattern recognition and oth...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2023-11-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2206039.pdf |
| _version_ | 1797633153647509504 |
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| author | CHEN Sugen, LIU Yufei |
| author_facet | CHEN Sugen, LIU Yufei |
| author_sort | CHEN Sugen, LIU Yufei |
| collection | DOAJ |
| description | Twin support vector clustering based on Hinge loss and twin support vector clustering based on Ramp loss are two new twin support vector clustering algorithms, which provide a new research idea for solving the clustering problem, and gradually become a research hotspot in pattern recognition and other fields. However, they often have poor performance when dealing with the clustering problem with noisy data. To solve this problem, in this paper, an asymmetric Ramp loss function is constructed and then an improved Ramp-based twin support vector clustering algorithm is also proposed. The asymmetric Ramp loss function not only inherits the advantages of the Ramp loss function, but also uses asymmetric bounded functions to measure the within-cluster and between-cluster scatters, which makes the algorithm more robust to data points far from the clustering center plane. The introduction of parameter t makes the asymmetric Ramp loss function more flexible. In particular, when t is equal to 1, the asymmetric Ramp loss function degenerates into Ramp loss function, such that the Ramp-based twin support vector clustering becomes a special case of proposed algorithm. In addition, its nonlinear clustering formation is also proposed via kernel trick. The non-convex optimization problems in linear and nonlinear models are solved effectively through the alternating iterative algorithm. Experiments are carried out on several benchmark UCI datasets and artificial datasets, and the experimental results verify the effectiveness of the proposed algorithm. |
| first_indexed | 2024-03-11T11:50:01Z |
| format | Article |
| id | doaj.art-a8d1045b60bf422db95f745043eb6103 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-03-11T11:50:01Z |
| publishDate | 2023-11-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-a8d1045b60bf422db95f745043eb61032023-11-09T08:18:08ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182023-11-0117112767277610.3778/j.issn.1673-9418.2206039Improved Ramp-Based Twin Support Vector ClusteringCHEN Sugen, LIU Yufei01. School of Mathematics and Physics, Anqing Normal University, Anqing, Anhui 246133, China 2. Key Laboratory of Modeling, Simulation and Control of Complex Ecosystem in Dabie Mountains of Anhui Higher Education Institutes, Anqing, Anhui 246133, China 3. International Joint Research Center of Simulation and Control for Population Ecology of Yangtze River in Anhui Province, Anqing, Anhui 246133, ChinaTwin support vector clustering based on Hinge loss and twin support vector clustering based on Ramp loss are two new twin support vector clustering algorithms, which provide a new research idea for solving the clustering problem, and gradually become a research hotspot in pattern recognition and other fields. However, they often have poor performance when dealing with the clustering problem with noisy data. To solve this problem, in this paper, an asymmetric Ramp loss function is constructed and then an improved Ramp-based twin support vector clustering algorithm is also proposed. The asymmetric Ramp loss function not only inherits the advantages of the Ramp loss function, but also uses asymmetric bounded functions to measure the within-cluster and between-cluster scatters, which makes the algorithm more robust to data points far from the clustering center plane. The introduction of parameter t makes the asymmetric Ramp loss function more flexible. In particular, when t is equal to 1, the asymmetric Ramp loss function degenerates into Ramp loss function, such that the Ramp-based twin support vector clustering becomes a special case of proposed algorithm. In addition, its nonlinear clustering formation is also proposed via kernel trick. The non-convex optimization problems in linear and nonlinear models are solved effectively through the alternating iterative algorithm. Experiments are carried out on several benchmark UCI datasets and artificial datasets, and the experimental results verify the effectiveness of the proposed algorithm.http://fcst.ceaj.org/fileup/1673-9418/PDF/2206039.pdfclustering; twin support vector clustering; loss function |
| spellingShingle | CHEN Sugen, LIU Yufei Improved Ramp-Based Twin Support Vector Clustering Jisuanji kexue yu tansuo clustering; twin support vector clustering; loss function |
| title | Improved Ramp-Based Twin Support Vector Clustering |
| title_full | Improved Ramp-Based Twin Support Vector Clustering |
| title_fullStr | Improved Ramp-Based Twin Support Vector Clustering |
| title_full_unstemmed | Improved Ramp-Based Twin Support Vector Clustering |
| title_short | Improved Ramp-Based Twin Support Vector Clustering |
| title_sort | improved ramp based twin support vector clustering |
| topic | clustering; twin support vector clustering; loss function |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2206039.pdf |
| work_keys_str_mv | AT chensugenliuyufei improvedrampbasedtwinsupportvectorclustering |