research-article

Enhanced tensor low-rank and sparse representation recovery for incomplete multi-view clustering

AUTHORs:

Chunlin ChenAuthors Info & Claims

AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence

Article No.: 1254, Pages 11174 - 11182

https://doi.org/10.1609/aaai.v37i9.26323

Published: 07 February 2023 Publication History

Abstract

Incomplete multi-view clustering (IMVC) has attracted remarkable attention due to the emergence of multi-view data with missing views in real applications. Recent methods attempt to recover the missing information to address the IMVC problem. However, they generally cannot fully explore the underlying properties and correlations of data similarities across views. This paper proposes a novel Enhanced Tensor Low-rank and Sparse Representation Recovery (ETL-SRR) method, which reformulates the IMVC problem as a joint incomplete similarity graph learning and complete tensor representation recovery problem. Specifically, ETLSRR learns the intra-view similarity graphs and constructs a 3-way tensor by stacking the graphs to explore the inter-view correlations. To alleviate the negative influence of missing views and data noise, ETLSRR decomposes the tensor into two parts: a sparse tensor and an intrinsic tensor, which models the noise and underlying true data similarities, respectively. Both global low-rank and local structured sparse characteristics of the intrinsic tensor are considered, which enhances the discrimination of similarity matrix. Moreover, instead of using the convex tensor nuclear norm, ETLSRR introduces a generalized nonconvex tensor low-rank regularization to alleviate the biased approximation. Experiments on several datasets demonstrate the effectiveness and superiority of our method compared with the state-of-the-art methods.

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Cited By

Ji JFeng SLi YBaeza-Yates RBonchi F(2024)Tensorized Unaligned Multi-view Clustering with Multi-scale Representation LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671689(1246-1256)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671689
Maggu JGoel A(2024)K-BEST subspace clustering: kernel-friendly block-diagonal embedded and similarity-preserving transformed subspace clusteringPattern Analysis & Applications10.1007/s10044-024-01336-227:4Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10044-024-01336-2

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AAAI'23/IAAI'23/EAAI'23: Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence

February 2023

16496 pages

ISBN:978-1-57735-880-0

Copyright © 2023 Association for the Advancement of Artificial Intelligence.

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Published: 07 February 2023

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Cited By

Ji JFeng SLi YBaeza-Yates RBonchi F(2024)Tensorized Unaligned Multi-view Clustering with Multi-scale Representation LearningProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671689(1246-1256)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671689
Maggu JGoel A(2024)K-BEST subspace clustering: kernel-friendly block-diagonal embedded and similarity-preserving transformed subspace clusteringPattern Analysis & Applications10.1007/s10044-024-01336-227:4Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s10044-024-01336-2

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