Visualisation of heterogeneous data with simultaneous feature saliency using Generalised Generative Topographic Mapping

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Most machine-learning algorithms are designed for datasets with features of a single type whereas very little attention has been given to datasets with mixed-type features. We recently proposed a model to handle mixed types with a probabilistic latent variable formalism. This proposed model describes the data by type-specific distributions that are conditionally independent given the latent space and is called generalised generative topographic mapping (GGTM). It has often been observed that visualisations of high-dimensional datasets can be poor in the presence of noisy features. In this paper we therefore propose to extend the GGTM to estimate feature saliency values (GGTMFS) as an integrated part of the parameter learning process with an expectation-maximisation (EM) algorithm. The efficacy of the proposed GGTMFS model is demonstrated both for synthetic and real datasets.



Publication date1 Oct 2015
Publication titleWorkshop new challenges in neural computation 2015
EditorsBarbara Hammer, Thomas Martinetz, Thomas Villmann
Place of PublicationBielefeld (DE)
PublisherUniversität Bielefeld
Number of pages8
Original languageEnglish
EventWorkshop new challenges in neural computation 2015 - Aachen, Germany

Publication series

NameMachine learning reports
PublisherUniversität Bielefeld
ISSN (Print)1865-3960


WorkshopWorkshop new challenges in neural computation 2015

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