Hypergraph-Supervised Deep Subspace Clustering

Yu Hu and Hongmin Cai
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Yu Hu: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China
Hongmin Cai: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China

Mathematics, 2021, vol. 9, issue 24, 1-15

Abstract: Auto-encoder (AE)-based deep subspace clustering (DSC) methods aim to partition high-dimensional data into underlying clusters, where each cluster corresponds to a subspace. As a standard module in current AE-based DSC, the self-reconstruction cost plays an essential role in regularizing the feature learning. However, the self-reconstruction adversely affects the discriminative feature learning of AE, thereby hampering the downstream subspace clustering. To address this issue, we propose a hypergraph-supervised reconstruction to replace the self-reconstruction. Specifically, instead of enforcing the decoder in the AE to merely reconstruct samples themselves, the hypergraph-supervised reconstruction encourages reconstructing samples according to their high-order neighborhood relations. By the back-propagation training, the hypergraph-supervised reconstruction cost enables the deep AE to capture the high-order structure information among samples, facilitating the discriminative feature learning and, thus, alleviating the adverse effect of the self-reconstruction cost. Compared to current DSC methods, relying on the self-reconstruction, our method has achieved consistent performance improvement on benchmark high-dimensional datasets.

Keywords: deep learning; computational intelligence; neural networks; deep subspace clustering; hypergraph (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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