Evidence Transfer for Improving Clustering Tasks Using External Categorical Evidence

In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome. By evidence transfer we define the process by whic...

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Vydáno v:Proceedings of ... International Joint Conference on Neural Networks s. 1 - 8
Hlavní autoři: Davvetas, Athanasios, Klampanos, Iraklis A., Karkaletsis, Vangelis
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.07.2019
Témata:
Autoencoders
Clustering
Combining Evidence
Deep Neural Networks
Entropy
Evidence Transfer
External Evidence
Generative Autoencoders
Image reconstruction
K-means Algorithm
Latent Space Manipulation
Meteorology
Neural networks
Task analysis
Training
Uncertainty
ISSN:2161-4407
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Shrnutí:In this paper we introduce evidence transfer for clustering, a deep learning method that can incrementally manipulate the latent representations of an autoencoder, according to external categorical evidence, in order to improve a clustering outcome. By evidence transfer we define the process by which the categorical outcome of an external, auxiliary task is exploited to improve a primary task, in this case representation learning for clustering. Our proposed method makes no assumptions regarding the categorical evidence presented, nor the structure of the latent space. We compare our method, against the baseline solution by performing k-means clustering before and after its deployment. Experiments with three different kinds of evidence show that our method effectively manipulates the latent representations when introduced with real corresponding evidence, while remaining robust when presented with low quality evidence.
ISSN:2161-4407
DOI:10.1109/IJCNN.2019.8852384