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Conference Paper/Proceeding/Abstract 984 views 315 downloads

Learning Discriminatory Deep Clustering Models

A. Alqahtani, Xianghua Xie Orcid Logo, J. Deng, Mark Jones Orcid Logo

Computer Analysis of Images and Patterns, Volume: 11678, Pages: 224 - 233

Swansea University Authors: Xianghua Xie Orcid Logo, Mark Jones Orcid Logo

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DOI (Published version): 10.1007/978-3-030-29888-3_18

Abstract

Deep convolutional auto-encoder (DCAE) allows to obtain useful features via its internal layer and provide an abstracted latent representation, which has been exploited for clustering analysis. DCAE allows a deep clustering method to extract similar patterns in lowerdimensional representation and fi...

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Published in: Computer Analysis of Images and Patterns
ISBN: 978-3-030-29887-6 978-3-030-29888-3
ISSN: 0302-9743 1611-3349
Published: 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa50907
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Abstract: Deep convolutional auto-encoder (DCAE) allows to obtain useful features via its internal layer and provide an abstracted latent representation, which has been exploited for clustering analysis. DCAE allows a deep clustering method to extract similar patterns in lowerdimensional representation and find idealistic representative centers for distributed data. In this paper, we present a deep clustering model carried out in presence of varying degrees of supervision. We propose a new version of DCAE to include a supervision component. It introduces a mechanism to inject various levels of supervision into the learning process. This mechanism helps to effectively reconcile extracted latent representations and provided supervising knowledge in order to produce the best discriminative attributes. The key idea of our approach is distinguishing the discriminatory power of numerous structures, through varying degrees of supervision, when searching for a compact structure to form robust clusters. We evaluate our model on MNIST, USPS, MNIST fashion, SVHN datasets and show clustering accuracy on different supervisory levels.
College: Faculty of Science and Engineering
Start Page: 224
End Page: 233

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