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

Shahzad Mumtaz, Michel F. Randrianandrasana, Gurjinder Bassi, Ian T. Nabney

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

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Abstract

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.
Original languageEnglish
Title of host publicationWorkshop new challenges in neural computation 2015
EditorsBarbara Hammer, Thomas Martinetz, Thomas Villmann
PublisherUniversität Bielefeld
Pages114-121
Number of pages8
Publication statusPublished - 1 Oct 2015

Publication series

NameMachine learning reports
PublisherUniversität Bielefeld

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