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

127Citations
Citations of this article
10Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

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. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 (Vol. 36, pp. 7576–7584). Association for the Advancement of Artificial Intelligence. https://doi.org/10.1609/aaai.v36i7.20723

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free