Learning Discriminatory Deep Clustering Models

Ali Alqahtani, Xianghua Xie, Jingjing Deng and Mark W. Jones


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.

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Ali Alqahtani, Xianghua Xie, Jingjing Deng and Mark W. Jones, Learning Discriminatory Deep Clustering Models. In: Vento M., Percannella G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science, vol 11678. Springer. 2019


author="Alqahtani, A.
and Xie, X.
and Deng, J.
and Jones, M. W.",
editor="Vento, Mario
and Percannella, Gennaro",
title="Learning Discriminatory Deep Clustering Models",
booktitle="Computer Analysis of Images and Patterns",
publisher="Springer International Publishing",