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A Deep Convolutional Auto-Encoder with Embedded Clustering / A. Alqahtani; X. Xie; J. Deng; M.W. Jones

2018 25th IEEE International Conference on Image Processing (ICIP), Pages: 4058 - 4062

Swansea University Author: Jones, Mark

  • Accepted Manuscript under embargo until: 6th September 2019

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...

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Published in: 2018 25th IEEE International Conference on Image Processing (ICIP)
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
Online Access: Check full text

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.
Keywords: Deep Learning, Deep Convolutional Auto-Encoder, Embedded Clustering
College: College of Science
Start Page: 4058
End Page: 4062