A Deep Convolutional Auto-Encoder With Embedded Clustering

A. Alqahtani, X. Xie, J. Deng, M. W. Jones

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In this paper, we propose a clustering approach embedded in a deep convolutional auto-encoder (DCAE). In contrast to conventional clustering approaches, our method simultaneously learns feature representations and cluster assignments through DCAEs. DCAEs have been effective in image processing as it fully utilizes the properties of convolutional neural networks. Our method consists of clustering and reconstruction objective functions. All data points are assigned to their new corresponding cluster centers during the optimization, after that, clustering centers are iteratively updated to obtain a stable performance of clustering. The experimental results on the MNIST dataset show that the proposed method substantially outperforms deep clustering models in term of clustering quality.

Deunyddiau Ffynhonnell

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DOI

10.1109/ICIP.2018.8451506
https://dx.doi.org/10.1109/ICIP.2018.8451506

Enwi

A. Alqahtani, X. Xie, J. Deng, M. W. Jones. A Deep Convolutional Auto-Encoder With Embedded Clustering. In 2018 IEEE International Conference on Image Processing (ICIP), Athens, Greece, 2018, pp. 4058-4062. doi:10.1109/ICIP.2018.8451506

Bibtex

@INPROCEEDINGS{DeepCAE, 
author={A. Alqahtani and X. Xie and J. Deng and M. W. Jones}, 
booktitle={2018 IEEE International Conference on Image Processing (ICIP)}, 
title={A Deep Convolutional Auto-Encoder With Embedded Clustering}, 
year={2018}, 
volume={}, 
number={}, 
pages={4058-4062}, 
doi={10.1109/ICIP.2018.8451506}, 
ISSN={2381-8549}, 
month={Oct}}