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Odor recognition in robotics applications by discriminative time-series modeling

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Abstract

Odor classification by a robot equipped with an electronic nose (e-nose) is a challenging task for pattern recognition since volatiles have to be classified quickly and reliably even in the case of short measurement sequences, gathered under operation in the field. Signals obtained in these circumstances are characterized by a high-dimensionality, which limits the use of classical classification techniques based on unsupervised and semi-supervised settings, and where predictive variables can be only identified using wrapper or post-processing techniques. In this paper, we consider generative topographic mapping through time (GTM-TT) as an unsupervised model for time-series inspection, based on hidden Markov models regularized by topographic constraints. We further extend the model such that supervised classification and relevance learning can be integrated, resulting in supervised GTM-TT. Then, we evaluate the suitability of this new technique for the odor classification problem in robotics applications. The performance is compared with classical techniques as nearest neighbor, as an absolute baseline, support vector machine and a recent time-series kernel approach, demonstrating the eligibility of our approach for high-dimensional data. Additionally, we exploit the learning system introduced in this work, providing a measure of the relevance of each sensor and individual time points in the classification process, from which important information can be extracted.

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Notes

  1. Figaro engineering inc. http://www.figaro.co.jp.

  2. e2v. http://www.e2v.com/.

  3. Grids: \({\lambda ,\gamma } = [0, 10^{-6} \ldots 10^{-1}, 0.5, 1 \ldots 5, 10, 30, 50, 100]\) \({\rm costs} = [0.1, 10, 10^2, 5 \times 10^2, 10^3, 5 \times 10^3, 10^4, 5 \times 10^4]\).

  4. Approaches for feature ranking by SVM are available but not for this type of data and not directly for multi-class problems as studied for DS1.

  5. Since butane is found at gas state at ambient temperature, the content of a lighter was released when the e-nose aspiration moved over the container.

  6. Here we simply used the model from the first crossvalidation run.

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Acknowledgments

The first author likes to thank Peter Tino, University of Birmingham, for interesting discussions about probabilistic modeling and Tien-ho Lin, Carnegie Mellon University, USA for support with the simulation data. Further, Ivan Olier, University of Manchaster, UK; Iain Strachan, AEA Technology, Harwell, UK and Markus Svensen, Microsoft Research, Cambridge, UK for providing code for GTM and GTM-TT. Further we would like to thank Fengzhen Tang, University of Birmingham for providing invaluable  support with the RTK method. This work was supported by the DFG project HA2719/4-1 to BH, by the DFG-NSF project TO 409/8-1, and by the Cluster of Excellence 277 CITEC funded in the framework of the German Excellence Initiative. Further, a Marie Curie Intra-European Fellowship (IEF): FP7-PEOPLE-2012-IEF (FP7-327791-ProMoS)  is gratefully acknowledged.  Additional support was provided by funds from the Andalucía Regional Government and the European Union (FEDER) under research project: TEP08-4016.

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Authors and Affiliations

  1. School of Computer Science, University of Birmingham, Birmingham, B15 2TT, UK

    Frank-Michael Schleif

  2. Center of Excellence, University of Bielefeld, Universitätsstrasse 21-23, 33615, Bielefeld, Germany

    Barbara Hammer

  3. Dpto. Ingenieria de Sistemas y Automatica E.T.S.I. Informatica, Telecomunicacion, Universidad de Malaga, Campus Universitario de Teatinos, 29071, Malaga, Spain

    Javier Gonzalez Monroy & Javier Gonzalez Jimenez

  4. Universidad de Almeria, Edificio CITE II-A, 04120, La Caada, Almeria, Spain

    Jose-Luis Blanco-Claraco

  5. Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen, P.O. Box 407, 9700 AK, Groningen, The Netherlands

    Michael Biehl & Nicolai Petkov

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  1. Frank-Michael Schleif
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  2. Barbara Hammer
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Correspondence to Frank-Michael Schleif.

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Schleif, FM., Hammer, B., Monroy, J.G. et al. Odor recognition in robotics applications by discriminative time-series modeling. Pattern Anal Applic 19, 207–220 (2016). https://doi.org/10.1007/s10044-014-0442-2

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