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Making nonlinear manifold learning models interpretable: the manifold grand tour

Author
Lisboa, P.; Martin, J.; Vellido, A.
Type of activity
Journal article
Journal
Expert systems with applications
Date of publication
2015-12
Volume
42
Number
22
First page
8982
Last page
8988
DOI
https://doi.org/10.1016/j.eswa.2015.07.054 Open in new window
Repository
http://hdl.handle.net/2117/78662 Open in new window
URL
http://www.sciencedirect.com/science/article/pii/S095741741500514X Open in new window
Abstract
Dimensionality reduction is required to produce visualisations of high dimensional data. In this framework, one of the most straightforward approaches to visualising high dimensional data is based on reducing complexity and applying linear projections while tumbling the projection axes in a defined sequence which generates a Grand Tour of the data. We propose using smooth nonlinear topographic maps of the data distribution to guide the Grand Tour, increasing the effectiveness of this approach by...
Citation
Lisboa, P., Martin, J., Vellido, A. Making nonlinear manifold learning models interpretable: the manifold grand tour. "Expert systems with applications", Desembre 2015, vol. 42, núm. 22, p. 8982-8988.
Keywords
Data visualization, Discriminative clustering, Generative topographic mapping, Grand tour, Linear projections, Manifold learning, Nonlinear dimensionality reduction
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SOCO - Soft Computing

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