A dimensionality-reduction technique inspired by receptor convergence in the olfactory system Academic Article

abstract

• In this paper, we propose a new technique for feature extraction/selection based on the projection of sensor features in class space while taking into account the sensor variance. The proposed technique is inspired by the organization of the early stages in the biological olfactory system. The algorithm proves to be highly suitable for high-dimensional feature vectors. The performance shows robustness with problems where only a small number of samples are available as a training dataset. We demonstrate the method on experimental data from two metal oxide sensors driven by a sinusoidal temperature profile. 2006 Elsevier B.V. All rights reserved.

authors

• Gutierrez-Osuna, Ricardo

published proceedings

• SENSORS AND ACTUATORS B-CHEMICAL

author list (cited authors)

• Perera, A., Yamanaka, T., Gutierrez-Galvez, A., Raman, B., & Gutierrez-Osuna, R.

citation count

• 21

complete list of authors

• Perera, A||Yamanaka, T||Gutierrez-Galvez, A||Raman, B||Gutierrez-Osuna, R

publication date

• January 2006

publisher

• Elsevier  Publisher

published in

• Sensors and Actuators, B: Chemical  Journal

keywords

• Bioinspired
• Electronic Nose
• Feature Selection
• Gas Sensor
• High Dimensionality
• Olfactory Model

Digital Object Identifier (DOI)

• 10.1016/j.snb.2005.11.082

start page

• 17

end page

• 22

volume

• 116

issue

• 1-2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.snb.2005.11.082