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Making nonlinear manifold learning models interpretable: the manifold grand tour

Autor
Lisboa, P.; Martin, J.; Vellido, A.
Tipus d'activitat
Article en revista
Revista
Expert systems with applications
Data de publicació
2015-12
Volum
42
Número
22
Pàgina inicial
8982
Pàgina final
8988
DOI
https://doi.org/10.1016/j.eswa.2015.07.054 Obrir en finestra nova
Repositori
http://hdl.handle.net/2117/78662 Obrir en finestra nova
URL
http://www.sciencedirect.com/science/article/pii/S095741741500514X Obrir en finestra nova
Resum
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...
Citació
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.
Paraules clau
Data visualization, Discriminative clustering, Generative topographic mapping, Grand tour, Linear projections, Manifold learning, Nonlinear dimensionality reduction
Grup de recerca
IDEAI-UPC Intelligent Data Science and Artificial Intelligence
SOCO - Soft Computing

Participants

Arxius