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

Internet

https://www.mdpi.com/2227-9709/6/2/17

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