Abstract
Clustering performs an essential role in many real world applications, such as market research, pattern recognition, data analysis, and image processing. However, due to the high dimensionality of the input feature values, the data being fed to clustering algorithms usually contains noise and thus could lead to in-accurate clustering results. While traditional dimension reduction and feature selection algorithms could be used to address this problem, the simple heuristic rules used in those algorithms are based on some particular assumptions. When those assumptions does not hold, these algorithms then might not work. In this paper, we propose DAC, Deep Autoencoder-based Clustering, a generalized data-driven framework to learn clustering representations using deep neuron networks. Experiment results show that our approach could effectively boost performance of the K-Means clustering algorithm on a variety types of datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 2, pp. 60–65 (2005)
Chen, F., Zhang, L., Yu, H.: External patch prior guided internal clustering for image denoising. In: IEEE International Conference on Computer Vision (ICCV), pp. 603–611 (2015)
Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, vol. 2. John Wiley & Sons Inc., New York (2001)
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, New York (2011)
Hosseinimotlagh, S., Papalexakis, E.E.: Unsupervised content-based identification of fake news articles with tensor decomposition ensembles. In: Proceedings of the Workshop on Misinformation and Misbehavior Mining on the Web (MIS2) (2018)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
Lu, S.: Good similar patches for image denoising. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1886–1895. IEEE (2019)
Lu, S., Ren, X., Liu, F.: Depth enhancement via low-rank matrix completion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3390–3397 (2014)
Pearson, K.: LIII. On lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci. 2(11), 559–572 (1901)
Yunchen, P., et al.: Variational autoencoder for deep learning of images, labels and captions. Adv. Neural Inf. Process. Syst. 29, 2352–2360 (2016)
Reyes-Ortiz, J.-L., Oneto, L., Samà, A., Parra, X., Anguita, D.: Transition-aware human activity recognition using smartphones. Neurocomputing 171, 754–767 (2016)
Sun, Q.-S., Zeng, S.-G., Liu, Y., Heng, P.-A., Xia, D.-S.: A new method of feature fusion and its application in image recognition. Pattern Recogn. 38(12), 2437–2448 (2005)
Xiao, H., Rasul, K., Vollgraf, R.: Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747 (2017)
Yang, X., Deng, C., Zheng, F., Yan, J., Liu, W.: Deep spectral clustering using dual autoencoder network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019
Zhao, Y., Karypis, G.: Evaluation of hierarchical clustering algorithms for document datasets. In: Proceedings of the Eleventh International Conference on Information and Knowledge Management, pp. 515–524 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Lu, S., Li, R. (2022). DAC–Deep Autoencoder-Based Clustering: A General Deep Learning Framework of Representation Learning. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 294. Springer, Cham. https://doi.org/10.1007/978-3-030-82193-7_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-82193-7_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82192-0
Online ISBN: 978-3-030-82193-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)