Robust anchor-based multi-view clustering via spectral embedded concept factorization
Multi-view clustering (MVC) often provides superior effectiveness to single-view clustering due to the integration of information from diverse views. Nonetheless, existing MVC methods are limited to large-scale real-world data by the drawbacks of low efficiency and poor robustness. To address these...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/172859 |
| _version_ | 1811686507362123776 |
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| author | Yang, Ben Wu, Jinghan Zhang, Xuetao Lin, Zhiping Nie, Feiping Chen, Badong |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Yang, Ben Wu, Jinghan Zhang, Xuetao Lin, Zhiping Nie, Feiping Chen, Badong |
| author_sort | Yang, Ben |
| collection | NTU |
| description | Multi-view clustering (MVC) often provides superior effectiveness to single-view clustering due to the integration of information from diverse views. Nonetheless, existing MVC methods are limited to large-scale real-world data by the drawbacks of low efficiency and poor robustness. To address these issues, we propose a novel robust anchor-based MVC model via spectral embedded concept factorization (RAMCSF). RAMCSF builds anchor graphs to approximate full-sample graphs and decomposes these anchor graphs by concept factorization (CF). To improve the clustering effectiveness, factor matrices of CF are constrained as orthogonal matrices to reduce the freedom of decomposition, and a novel small-scale anchor-based spectral embedding is designed to explore the high-order neighbor relationships. To restrain complex noises distributed in real-world data, we employ correntropy to measure the error between the original data and the learned representation. Moreover, RAMCSF can get a clustering indicator matrix directly, avoiding additional post-processing and ensuring that changes in data dimensions have a limited impact on efficiency. The model is then optimized by a novel fast half-quadratic-based optimization strategy that combines the orthogonal properties and the traces of matrices. Extensive experiments indicate that RAMCSF can achieve higher efficiency and robustness while maintaining comparable effectiveness to other state-of-the-art methods. |
| first_indexed | 2024-10-01T05:01:31Z |
| format | Journal Article |
| id | ntu-10356/172859 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:01:31Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1728592023-12-27T01:56:18Z Robust anchor-based multi-view clustering via spectral embedded concept factorization Yang, Ben Wu, Jinghan Zhang, Xuetao Lin, Zhiping Nie, Feiping Chen, Badong School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Multi-View Clustering Concept Factorization Multi-view clustering (MVC) often provides superior effectiveness to single-view clustering due to the integration of information from diverse views. Nonetheless, existing MVC methods are limited to large-scale real-world data by the drawbacks of low efficiency and poor robustness. To address these issues, we propose a novel robust anchor-based MVC model via spectral embedded concept factorization (RAMCSF). RAMCSF builds anchor graphs to approximate full-sample graphs and decomposes these anchor graphs by concept factorization (CF). To improve the clustering effectiveness, factor matrices of CF are constrained as orthogonal matrices to reduce the freedom of decomposition, and a novel small-scale anchor-based spectral embedding is designed to explore the high-order neighbor relationships. To restrain complex noises distributed in real-world data, we employ correntropy to measure the error between the original data and the learned representation. Moreover, RAMCSF can get a clustering indicator matrix directly, avoiding additional post-processing and ensuring that changes in data dimensions have a limited impact on efficiency. The model is then optimized by a novel fast half-quadratic-based optimization strategy that combines the orthogonal properties and the traces of matrices. Extensive experiments indicate that RAMCSF can achieve higher efficiency and robustness while maintaining comparable effectiveness to other state-of-the-art methods. This work was supported in part by the National Natural Science Foundation of China (No. 62088102), the Natural Science Basic Research Program of Shaanxi (2023-JC-YB-486), and the Fundamental Research Funds for the Central Universities (No. xzy022021044). 2023-12-27T01:56:18Z 2023-12-27T01:56:18Z 2023 Journal Article Yang, B., Wu, J., Zhang, X., Lin, Z., Nie, F. & Chen, B. (2023). Robust anchor-based multi-view clustering via spectral embedded concept factorization. Neurocomputing, 528, 136-147. https://dx.doi.org/10.1016/j.neucom.2023.01.028 0925-2312 https://hdl.handle.net/10356/172859 10.1016/j.neucom.2023.01.028 2-s2.0-85146897831 528 136 147 en Neurocomputing © 2023 Published by Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Multi-View Clustering Concept Factorization Yang, Ben Wu, Jinghan Zhang, Xuetao Lin, Zhiping Nie, Feiping Chen, Badong Robust anchor-based multi-view clustering via spectral embedded concept factorization |
| title | Robust anchor-based multi-view clustering via spectral embedded concept factorization |
| title_full | Robust anchor-based multi-view clustering via spectral embedded concept factorization |
| title_fullStr | Robust anchor-based multi-view clustering via spectral embedded concept factorization |
| title_full_unstemmed | Robust anchor-based multi-view clustering via spectral embedded concept factorization |
| title_short | Robust anchor-based multi-view clustering via spectral embedded concept factorization |
| title_sort | robust anchor based multi view clustering via spectral embedded concept factorization |
| topic | Engineering::Electrical and electronic engineering Multi-View Clustering Concept Factorization |
| url | https://hdl.handle.net/10356/172859 |
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