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A state-space model on interactive dimensionality reduction

Author:
Díaz Blanco, IgnacioUniovi authority; Cuadrado Vega, Abel AlbertoUniovi authority; Verleysen, Michel
Publication date:
2016
Editorial:

i6doc.com publication

Citación:
ESANN 2016 - 24th European Symposium on Artificial Neural Networks, p. 647-652 (2016)
Descripción física:
p. 647-652
Abstract:

In this work, we present a conceptual approach to the convergence dynamics of interactive dimensionality reduction (iDR) algorithms from the perspective of a well stablished theoretical model, namely statespace theory. The expected benefits are twofold: 1) suggesting new ways to import well known ideas from the state-space theory that help in the characterization and development of iDR algorithms and 2) providing a conceptual model for user interaction in iDR algorithms, that can be easily adopted for future interactive machine learning (iML) tools

In this work, we present a conceptual approach to the convergence dynamics of interactive dimensionality reduction (iDR) algorithms from the perspective of a well stablished theoretical model, namely statespace theory. The expected benefits are twofold: 1) suggesting new ways to import well known ideas from the state-space theory that help in the characterization and development of iDR algorithms and 2) providing a conceptual model for user interaction in iDR algorithms, that can be easily adopted for future interactive machine learning (iML) tools

Description:

24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016. 27 April 2016 through 29 April 2016

URI:
http://hdl.handle.net/10651/40683
ISBN:
9782875870278
Patrocinado por:

The authors would like to thank financial support from the Spanish Ministry of Economy (MINECO) and FEDER funds from the EU under grant DPI2015-69891-C2-2-R

Id. Proyecto:

MINECO/DPI2015-69891-C2-2-R

EU/DPI2015-69891-C2-2-R

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