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Self-supervised deep subspace clustering with entropy-norm.
- Source :
-
Cluster Computing . Apr2024, Vol. 27 Issue 2, p1611-1623. 13p. - Publication Year :
- 2024
-
Abstract
- Auto-Encoder based Deep Subspace Clustering (DSC) has been widely applied in computer vision, motion segmentation and image processing. However, existing DSC methods suffer from two limitations: (1) they ignore the rich useful relational information and the connectivity within each subspace due to the reconstruction loss; (2) they design convolutional networks individually according to specific datasets. To address the above problems and improve the performance of DSC, we propose a novel algorithm called Self-Supervised deep Subspace Clustering with Entropy-norm(S 3 CE) in this paper. Firstly, S 3 CE introduces self-supervised contrastive learning to pre-train the encoder instead of requiring a decoder. Besides, the trained encoder is used as a feature extractor to segment subspace by combining self-expression layer and entropy-norm constraint. This not only preserves the local structure of data, but also improves the connectivity between data points. Extensive experimental results demonstrate the superior performance of S 3 CE in comparison to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13867857
- Volume :
- 27
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Cluster Computing
- Publication Type :
- Academic Journal
- Accession number :
- 176384331
- Full Text :
- https://doi.org/10.1007/s10586-023-04033-7