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The Effect of Evidence Transfer on Latent Feature Relevance for Clustering

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Institute of Informatics and Telecommunications, National Centre for Scientific Research “Demokritos”, Agia Paraskevi, 15310 Athens, Greece
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Department of Informatics and Telecommunications, University of Peloponnese, 22100 Tripoli, Greece
*
Author to whom correspondence should be addressed.
Informatics 2019, 6(2), 17; https://doi.org/10.3390/informatics6020017
Received: 30 March 2019 / Revised: 19 April 2019 / Accepted: 22 April 2019 / Published: 25 April 2019
(This article belongs to the Special Issue Feature Selection Meets Deep Learning)
Evidence transfer for clustering is a deep learning method that manipulates the latent representations of an autoencoder according to external categorical evidence with the effect of improving a clustering outcome. Evidence transfer’s application on clustering is designed to be robust when introduced with a low quality of evidence, while increasing the effectiveness of the clustering accuracy during relevant corresponding evidence. We interpret the effects of evidence transfer on the latent representation of an autoencoder by comparing our method to the information bottleneck method. Information bottleneck is an optimisation problem of finding the best tradeoff between maximising the mutual information of data representations and a task outcome while at the same time being effective in compressing the original data source. We posit that the evidence transfer method has essentially the same objective regarding the latent representations produced by an autoencoder. We verify our hypothesis using information theoretic metrics from feature selection in order to perform an empirical analysis over the information that is carried through the bottleneck of the latent space. We use the relevance metric to compare the overall mutual information between the latent representations and the ground truth labels before and after their incremental manipulation, as well as, to study the effects of evidence transfer regarding the significance of each latent feature. View Full-Text
Keywords: deep neural networks; evidence transfer; relevance; feature selection; information bottleneck; latent features; deep learning deep neural networks; evidence transfer; relevance; feature selection; information bottleneck; latent features; deep learning
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Davvetas, A.; Klampanos, I.A.; Skiadopoulos, S.; Karkaletsis, V. The Effect of Evidence Transfer on Latent Feature Relevance for Clustering. Informatics 2019, 6, 17.

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