The Effect of Evidence Transfer on Latent Feature Relevance for Clustering

The Effect of Evidence Transfer on Latent Feature Relevance for Clustering

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...

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Bibliographic Details
Main Authors:	Athanasios Davvetas, Iraklis A. Klampanos, Spiros Skiadopoulos, Vangelis Karkaletsis
Format:	Article
Language:	English
Published:	MDPI AG 2019-04-01
Series:	Informatics
Subjects:	deep neural networks evidence transfer relevance feature selection information bottleneck latent features deep learning
Online Access:	https://www.mdpi.com/2227-9709/6/2/17

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