Deep clustering techniques based on autoencoders
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48743-9_11
Reference15 articles.
1. Bank, D., Koenigstein, N., and Giryes, R. (2023). Autoencoders. Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, pages 353–374.
2. Cai, J., Wang, S., and Guo, W. (2021). Unsupervised embedded feature learning for deep clustering with stacked sparse auto-encoder. Expert Systems with Applications, 186:115729.
3. Chen, R., Tang, Y., Tian, L., Zhang, C., and Zhang, W. (2022). Deep convolutional self-paced clustering. Applied Intelligence, 52(5):4858–4872.
4. Ghasedi Dizaji, K., Herandi, A., Deng, C., Cai, W., and Huang, H. (2017). Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization. In Proceedings of the IEEE international conference on computer vision, pages 5736–5745.
5. Guo, X., Gao, L., Liu, X., and Yin, J. (2017). Improved deep embedded clustering with local structure preservation. In Ijcai, pages 1753–1759.
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