Conference Paper/Proceeding/Abstract 1610 views 586 downloads
A Deep Convolutional Auto-Encoder with Embedded Clustering
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Xianghua Xie ,
Jingjing Deng
2018 25th IEEE International Conference on Image Processing (ICIP), Pages: 4058 - 4062
Swansea University Authors:
Mark Jones , Xianghua Xie , Jingjing Deng
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PDF | Accepted Manuscript
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DOI (Published version): 10.1109/ICIP.2018.8451506
Abstract
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...
| Published in: | 2018 25th IEEE International Conference on Image Processing (ICIP) |
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| ISBN: | 978-1-4799-7062-9 978-1-4799-7061-2 |
| ISSN: | 2381-8549 |
| Published: |
Megaron Athens International Conference Centre, Athens, Greece
25th IEEE International Conference on Image Processing (ICIP)
2018
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa40682 |
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| Abstract: |
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. |
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| Keywords: |
Deep Learning, Deep Convolutional Auto-Encoder, Embedded Clustering |
| College: |
Faculty of Science and Engineering |
| Start Page: |
4058 |
| End Page: |
4062 |