RT Journal Article
T1 Odor recognition in robotics applications by discriminative time-series modeling
A1 Schleif, Frank Michel
A1 Hammer, Barbara
A1 González-Monroy, Javier
A1 Gonzalez-Jimenez, Antonio Javier
A1 Blanco-Claraco, José Luis
A1 Biehl, Michael
A1 Petkov, Nicolai
K1 Robótica - Diseño y construcción
AB 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.
YR 2015
FD 2015-01-08
LK https://hdl.handle.net/10630/33071
UL https://hdl.handle.net/10630/33071
LA eng
NO Pattern Anal Applic (2016) 19:207–220 DOI 10.1007/s10044-014-0442-2
NO 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 An 545 daluc´ıa Regional Government and the European Union (FEDER) under research project: TEP08-4016
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 27 ene 2026