Multi-view Subspace Clustering via An Adaptive Consensus Graph Filter
Authors: 
Lai Wei, Shanshan SongAuthors Info & Claims
Lai Wei, Shanshan SongAuthors Info & ClaimsPages 776 - 784
Abstract
Multiview subspace clustering (MVSC) has attracted an increasing amount of attention in recent years. Most existing MVSC methods first collect complementary information from different views and consequently derive a consensus reconstruction coefficient matrix to indicate the subspace structure of a multi-view data set. In this paper, we initially assume the existence of a consensus reconstruction coefficient matrix and then use it to build a consensus graph filter. In each view, the filter is employed for smoothing the data and designing a regularizer for the reconstruction coefficient matrix. Finally, the obtained reconstruction coefficient matrices from different views are used to create constraints for the consensus reconstruction coefficient matrix. Therefore, in the proposed method, the consensus reconstruction coefficient matrix, the consensus graph filter, and the reconstruction coefficient matrices from different views are interdependent. We provide an optimization algorithm to obtain their optimal values. Extensive experiments on diverse multi-view data sets demonstrate that our approach outperforms some state-of-the-art methods.
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Published In
May 2024
1379 pages
ISBN:9798400706196
DOI:10.1145/3652583
- General Chairs:
- Cathal Gurrin,
- Rachada Kongkachandra,
- Klaus Schoeffmann,
- Program Chairs:
- Duc-Tien Dang-Nguyen,
- Luca Rossetto,
- Shin'ichi Satoh,
- Liting Zhou
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Published: 07 June 2024
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