Correntropy based graph regularized concept factorization for clustering
Concept factorization (CF) technique is one of the most powerful approaches for feature learning, and has been successfully adopted in a wide range of practical applications such as data mining, computer vision, and information retrieval. Most existing concept factorization methods mainly minimize t...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/136678 |
| _version_ | 1811682741851258880 |
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| author | Peng, Siyuan Ser, Wee Chen, Badong Sun, Lei Lin, Zhiping |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Peng, Siyuan Ser, Wee Chen, Badong Sun, Lei Lin, Zhiping |
| author_sort | Peng, Siyuan |
| collection | NTU |
| description | Concept factorization (CF) technique is one of the most powerful approaches for feature learning, and has been successfully adopted in a wide range of practical applications such as data mining, computer vision, and information retrieval. Most existing concept factorization methods mainly minimize the square of the Euclidean distance, which is seriously sensitive to non-Gaussian noises or outliers in the data. To alleviate the adverse influence of this limitation, in this paper, a robust graph regularized concept factorization method, called correntropy based graph regularized concept factorization (GCCF), is proposed for clustering tasks. Specifically, based on the maximum correntropy criterion (MCC), GCCF is derived by incorporating the graph structure information into our proposed objective function. A half-quadratic optimization technique is adopted to solve the non-convex objective function of the GCCF method effectively. In addition, algorithm analysis of GCCF is studied. Extensive experiments on real world datasets demonstrate that the proposed GCCF method outperforms seven competing methods for clustering applications. |
| first_indexed | 2024-10-01T04:01:40Z |
| format | Journal Article |
| id | ntu-10356/136678 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:01:40Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1366782020-01-10T02:36:30Z Correntropy based graph regularized concept factorization for clustering Peng, Siyuan Ser, Wee Chen, Badong Sun, Lei Lin, Zhiping School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Engineering::Computer science and engineering Concept Factorization Graph Regularization Concept factorization (CF) technique is one of the most powerful approaches for feature learning, and has been successfully adopted in a wide range of practical applications such as data mining, computer vision, and information retrieval. Most existing concept factorization methods mainly minimize the square of the Euclidean distance, which is seriously sensitive to non-Gaussian noises or outliers in the data. To alleviate the adverse influence of this limitation, in this paper, a robust graph regularized concept factorization method, called correntropy based graph regularized concept factorization (GCCF), is proposed for clustering tasks. Specifically, based on the maximum correntropy criterion (MCC), GCCF is derived by incorporating the graph structure information into our proposed objective function. A half-quadratic optimization technique is adopted to solve the non-convex objective function of the GCCF method effectively. In addition, algorithm analysis of GCCF is studied. Extensive experiments on real world datasets demonstrate that the proposed GCCF method outperforms seven competing methods for clustering applications. Accepted version 2020-01-10T02:36:30Z 2020-01-10T02:36:30Z 2018 Journal Article Peng, S., Ser, W., Chen, B., Sun, L., & Lin, Z. (2018). Correntropy based graph regularized concept factorization for clustering. Neurocomputing, 316, 34-48. doi:10.1016/j.neucom.2018.07.049 0925-2312 https://hdl.handle.net/10356/136678 10.1016/j.neucom.2018.07.049 2-s2.0-85051654621 316 34 48 en Neurocomputing © 2018 Elsevier B.V. All rights reserved. This paper was published in Neurocomputing and is made available with permission of Elsevier B.V. application/pdf |
| spellingShingle | Engineering::Electrical and electronic engineering Engineering::Computer science and engineering Concept Factorization Graph Regularization Peng, Siyuan Ser, Wee Chen, Badong Sun, Lei Lin, Zhiping Correntropy based graph regularized concept factorization for clustering |
| title | Correntropy based graph regularized concept factorization for clustering |
| title_full | Correntropy based graph regularized concept factorization for clustering |
| title_fullStr | Correntropy based graph regularized concept factorization for clustering |
| title_full_unstemmed | Correntropy based graph regularized concept factorization for clustering |
| title_short | Correntropy based graph regularized concept factorization for clustering |
| title_sort | correntropy based graph regularized concept factorization for clustering |
| topic | Engineering::Electrical and electronic engineering Engineering::Computer science and engineering Concept Factorization Graph Regularization |
| url | https://hdl.handle.net/10356/136678 |
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