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


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.

Divisions: Engineering & Applied Sciences
Engineering & Applied Sciences > Computer Science
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: © the authors
Event Title: Workshop new challenges in neural computation 2015
Event Type: Other
Event Location: Informatikzentrum of RWTH Aachen
Event Dates: 2015-10-10 - 2015-10-10
Uncontrolled Keywords: Computer Vision and Pattern Recognition
Full Text Link: ... mlr_03_2015.pdf
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PURE Output Type: Conference contribution
Published Date: 2015-10-01
Authors: Mumtaz, Shahzad
Randrianandrasana, Michel F. ( 0000-0002-4181-1323)
Bassi, Gurjinder
Nabney, Ian T. ( 0000-0003-1513-993X)



Version: Accepted Version

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