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...

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Main Authors: Yang, Ben, Wu, Jinghan, Zhang, Xuetao, Lin, Zhiping, Nie, Feiping, Chen, Badong
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2023
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Online Access:https://hdl.handle.net/10356/172859
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.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.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
Multi-View Clustering
Concept Factorization
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
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.
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
format Article
author Yang, Ben
Wu, Jinghan
Zhang, Xuetao
Lin, Zhiping
Nie, Feiping
Chen, Badong
author_sort Yang, Ben
title 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_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_sort robust anchor-based multi-view clustering via spectral embedded concept factorization
publishDate 2023
url https://hdl.handle.net/10356/172859
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