@article{d626ca7c745d43b3a4ae451f5e5af306,
title = "A dimensionality-reduction technique inspired by receptor convergence in the olfactory system",
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.",
keywords = "Bioinspired, Electronic nose, Feature selection, Gas sensor, High dimensionality, Olfactory model",
author = "A. Perera and T. Yamanaka and A. Guti{\'e}rrez-G{\'a}lvez and B. Raman and R. Guti{\'e}rrez-Osuna",
note = "Funding Information: This work was supported by the National Science Foundation under CAREER Grant No. 9984426/0229598. Funding Information: Takao Yamanaka received the Bachelor of Engineering, the Master of Engineering, and the PhD in engineering from Tokyo Institute of Technology in 1996, 1998, and 2004,respectively. He was with Canon, Inc. from 1998 to 2000 in the area of image and signal processing. He is currently a postdoctoral fellow at Texas A&M University, supported by JSPS postdoctoral fellowship for research abroad. His research interests include neuromorphic engineering, intelligent sensors, machine learning, computational neuroscience, and machine olfaction. ",
year = "2006",
month = jul,
day = "28",
doi = "10.1016/j.snb.2005.11.082",
language = "English",
volume = "116",
pages = "17--22",
journal = "Sensors and Actuators, B: Chemical",
issn = "0925-4005",
number = "1-2",
}