A state-space model on interactive dimensionality reduction
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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
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24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2016. 27 April 2016 through 29 April 2016
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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
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