The DOI matches its Crossref record; 57 of the 57 references it cites were found in a registry or an index.
Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization
Open accessAbstract
In this paper, we propose a new clustering model, called DEeP Embedded Regularized ClusTering (DEPICT), which efficiently maps data into a discriminative embedding subspace and precisely predicts cluster assignments. DEPICT generally consists of a multinomial logistic regression function stacked on top of a multi-layer convolutional autoencoder. We define a clustering objective function using relative entropy (KL divergence) minimization, regularized by a prior for the frequency of cluster assignments. An alternating strategy is then derived to optimize the objective by updating parameters and estimating cluster assignments. Furthermore, we employ the reconstruction loss functions in our autoencoder, as a data-dependent regularization term, to prevent the deep embedding function from overfitting. In order to benefit from end-to-end optimization and eliminate the necessity for layer-wise pre-training, we introduce a joint learning framework to minimize the unified clustering and reconstruction loss functions together and train all network layers simultaneously. Experimental results indicate the superiority and faster running time of DEPICT in real-world clustering tasks, where no labeled data is available for hyper-parameter tuning.
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Cited by
Li, Y., Hu, P., Liu, Z., Peng, D., Zhou, J. T., & Peng, X. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i10.17037 · Cited by 714
Attributed Graph Clustering: A Deep Attentional Embedding Approach
Wang, C., Pan, S., Hu, R., Long, G., Jiang, J., & Zhang, C. (2019) · Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2019/509 · Cited by 573
A Survey of Clustering With Deep Learning: From the Perspective of Network Architecture
Min, E., Guo, X., Liu, Q., Zhang, G., Cui, J., & Long, J. (2018) · IEEE Access · doi:10.1109/access.2018.2855437 · Cited by 553
Deep Spectral Clustering Using Dual Autoencoder Network
Yang, X., Deng, C., Zheng, F., Yan, J., & Liu, W. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00419 · Cited by 313
Self-citationLearning to Discover Novel Visual Categories via Deep Transfer Clustering
Han, K., Vedaldi, A., & Zisserman, A. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00849 · Cited by 303
Metadata
- Authors
- Dizaji, K. G., Herandi, A., Deng, C., Cai, W., & Huang, H.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 5747-5756
- OpenAlex
- W2608862709
- Cited by
- 579
- References
- 57
- FWCI
- 23.50
- Type
- conference paper
- Language
- en
- Open access
- green
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- Dizaji, K. G., Herandi, A., Deng, C., Cai, W., & Huang, H. (2017). Deep Clustering via Joint Convolutional Autoencoder Embedding and Relative Entropy Minimization. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.612
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