Deep Clustering with Convolutional Autoencoders

  • Xifeng GuoEmail author
  • Xinwang Liu
  • En Zhu
  • Jianping Yin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)


Deep clustering utilizes deep neural networks to learn feature representation that is suitable for clustering tasks. Though demonstrating promising performance in various applications, we observe that existing deep clustering algorithms either do not well take advantage of convolutional neural networks or do not considerably preserve the local structure of data generating distribution in the learned feature space. To address this issue, we propose a deep convolutional embedded clustering algorithm in this paper. Specifically, we develop a convolutional autoencoders structure to learn embedded features in an end-to-end way. Then, a clustering oriented loss is directly built on embedded features to jointly perform feature refinement and cluster assignment. To avoid feature space being distorted by the clustering loss, we keep the decoder remained which can preserve local structure of data in feature space. In sum, we simultaneously minimize the reconstruction loss of convolutional autoencoders and the clustering loss. The resultant optimization problem can be effectively solved by mini-batch stochastic gradient descent and back-propagation. Experiments on benchmark datasets empirically validate the power of convolutional autoencoders for feature learning and the effectiveness of local structure preservation.


Deep clustering Convolutional autoencoders Convolutional neural networks Unsupervised learning 



This work was financially supported by the National Natural Science Foundation of China (Project no. 60970034, 61170287, 61232016 and 61672528).


  1. 1.
    Chen, G.: Deep learning with nonparametric clustering. arXiv preprint arXiv:1501.03084 (2015)
  2. 2.
    Chollet, F., et al.: Keras (2015).
  3. 3.
    Glorot, X., Bordes, A., Bengio, Y.: Deep sparse rectifier neural networks. J. Mach. Learn. Res. 15, 315–323 (2011)Google Scholar
  4. 4.
    Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016)Google Scholar
  5. 5.
    Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning (ICML), pp. 448–456 (2015)Google Scholar
  6. 6.
    Kingma, D., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
  7. 7.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  8. 8.
    Li, F., Qiao, H., Zhang, B., Xi, X.: Discriminatively boosted image clustering with fully convolutional auto-encoders. arXiv preprint arXiv:1703.07980 (2017)
  9. 9.
    Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008)zbMATHGoogle Scholar
  10. 10.
    Masci, J., Meier, U., Cireşan, D., Schmidhuber, J.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011. LNCS, vol. 6791, pp. 52–59. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21735-7_7 CrossRefGoogle Scholar
  11. 11.
    Nie, F., Zeng, Z., Tsang, I.W., Xu, D., Zhang, C.: Spectral embedded clustering: a framework for in-sample and out-of-sample spectral clustering. IEEE Trans. Neural Netw. 22(11), 1796–1808 (2011)CrossRefGoogle Scholar
  12. 12.
    Peng, X., Feng, J., Lu, J., Yau, W.Y., Yi, Z.: Cascade subspace clustering. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 2478–2484 (2017)Google Scholar
  13. 13.
    Peng, X., Xiao, S., Feng, J., Yau, W.Y., Yi, Z.: Deep subspace clustering with sparsity prior. In: International Joint Conference on Artificial Intelligence (IJCAI) (2016)Google Scholar
  14. 14.
    Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)zbMATHMathSciNetGoogle Scholar
  15. 15.
    Tian, F., Gao, B., Cui, Q., Chen, E., Liu, T.Y.: Learning deep representations for graph clustering. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 1293–1299 (2014)Google Scholar
  16. 16.
    Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning (ICML) (2016)Google Scholar
  17. 17.
    Guo, X., Gao, L., Liu, X., Yin, J.: Improved deep embedded clustering with local structure preservation. In: International Joint Conference on Artificial Intelligence (IJCAI-17), pp. 1753–1759 (2017). doi: 10.24963/ijcai.2017/243
  18. 18.
    Yang, B., Fu, X., Sidiropoulos, N.D., Hong, M.: Towards k-means-friendly spaces: simultaneous deep learning and clustering. arXiv preprint arXiv:1610.04794 (2016)
  19. 19.
    Yang, J., Parikh, D., Batra, D.: Joint unsupervised learning of deep representations and image clusters. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5147–5156 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.College of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.State Key Laboratory of High Performance ComputingNational University of Defense TechnologyChangshaChina

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