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Tensorized Unaligned Multi-view Clustering with Multi-scale Representation Learning

Published: 24 August 2024 Publication History

Abstract

The Unaligned Multi-view Clustering (UMC) problem is currently receiving widespread attention, focusing on clustering unaligned multi-view data generated in real-world applications. Although some algorithms have emerged to address this issue, there still exist the following drawbacks: 1) The fully unknown correspondence of samples across views can significantly limit the exploration of consistent clustering structure. 2) The fixed representation space makes it difficult to mine the comprehensive information in the original data. 3) Unbiased tensor rank approximation is desired to capture the high-order correlation among different views. To address these issues, we proposed a novel UMC framework termed Tensorized Unaligned Multi-view Clustering with Multi-scale Representation Learning (TUMCR). Specifically, TUMCR designs a multi-scale representation learning and alignment framework, which constructs multi-scale representation spaces to comprehensively explore the unknown correspondence across views. Then, a tensorial multi-scale fusion module is proposed to fuse multi-scale representations and explore the high-order correlation hidden in different views, which utilizes the Enhanced Tensor Rank (ETR) to learn the low-rank structure. Furthermore, TUMCR is solved by an efficient algorithm with good convergence. Extensive experiments on different types of datasets demonstrate the effectiveness and superiority of our TUMCR compared with state-of-the-art methods. Our code is publicly available at: https://github.com/jijintian/TUMCR.

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cover image ACM Conferences
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 2024
6901 pages
ISBN:9798400704901
DOI:10.1145/3637528
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Published: 24 August 2024

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Author Tags

  1. low-rank tensor regularization
  2. multi-scale representations
  3. unaligned multi-view clustering

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  • Research-article

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  • The National Natural Science Foundation of China under Grant

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