Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors

Authors

  • Suyuan Liu School of Computer, National University of Defense Technology
  • Siwei Wang School of Computer, National University of Defense Technology
  • Pei Zhang School of Computer, National University of Defense Technology
  • Kai Xu School of Computer, National University of Defense Technology
  • Xinwang Liu School of Computer, National University of Defense Technology
  • Changwang Zhang CCF Theoretical Computer Science Technical Committee
  • Feng Gao School of Arts, Peking University

DOI:

https://doi.org/10.1609/aaai.v36i7.20723

Keywords:

Machine Learning (ML)

Abstract

Multi-view subspace clustering (MVSC) optimally integrates multiple graph structure information to improve clustering performance. Recently, many anchor-based variants are proposed to reduce the computational complexity of MVSC. Though achieving considerable acceleration, we observe that most of them adopt fixed anchor points separating from the subsequential anchor graph construction, which may adversely affect the clustering performance. In addition, post-processing is required to generate discrete clustering labels with additional time consumption. To address these issues, we propose a scalable and parameter-free MVSC method to directly output the clustering labels with optimal anchor graph, termed as Efficient One-pass Multi-view Subspace Clustering with Consensus Anchors (EOMSC-CA). Specially, we combine anchor learning and graph construction into a uniform framework to boost clustering performance. Meanwhile, by imposing a graph connectivity constraint, our algorithm directly outputs the clustering labels without any post-processing procedures as previous methods do. Our proposed EOMSC-CA is proven to be linear complexity respecting to the data size. The superiority of our EOMSC-CA over the effectiveness and efficiency is demonstrated by extensive experiments. Our code is publicly available at https://github.com/Tracesource/EOMSC-CA.

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Published

2022-06-28

How to Cite

Liu, S., Wang, S., Zhang, P., Xu, K., Liu, X., Zhang, C., & Gao, F. (2022). Efficient One-Pass Multi-View Subspace Clustering with Consensus Anchors. Proceedings of the AAAI Conference on Artificial Intelligence, 36(7), 7576-7584. https://doi.org/10.1609/aaai.v36i7.20723

Issue

Section

AAAI Technical Track on Machine Learning II