Self-Supervised Deep Subspace Clustering with Entropy-norm

• URL: http://arxiv.org/abs/2206.04958v1
• Date: Fri, 10 Jun 2022 09:15:33 GMT
• Title: Self-Supervised Deep Subspace Clustering with Entropy-norm
• Authors: Guangyi Zhao and Simin Kou and Xuesong Yin
• Abstract summary: Self-Supervised deep Subspace Clustering with Entropy-norm (S$3$CE) S$3$CE exploits a self-supervised contrastive network to gain a more effetive feature vector. New module with data enhancement is designed to help S$3$CE focus on the key information of data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Auto-Encoder based deep subspace clustering (DSC) is widely used in computer vision, motion segmentation and image processing. However, it suffers from the following three issues in the self-expressive matrix learning process: the first one is less useful information for learning self-expressive weights due to the simple reconstruction loss; the second one is that the construction of the self-expression layer associated with the sample size requires high-computational cost; and the last one is the limited connectivity of the existing regularization terms. In order to address these issues, in this paper we propose a novel model named Self-Supervised deep Subspace Clustering with Entropy-norm (S$^{3}$CE). Specifically, S$^{3}$CE exploits a self-supervised contrastive network to gain a more effetive feature vector. The local structure and dense connectivity of the original data benefit from the self-expressive layer and additional entropy-norm constraint. Moreover, a new module with data enhancement is designed to help S$^{3}$CE focus on the key information of data, and improve the clustering performance of positive and negative instances through spectral clustering. Extensive experimental results demonstrate the superior performance of S$^{3}$CE in comparison to the state-of-the-art approaches.

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