A dimensionality-reduction technique inspired by receptor convergence in the olfactory system
نویسندگان
چکیده
In this paper we propose a new technique for feature extraction/selection based on the projection of sensor features in class space and taking into account the sensor variance. The proposed technique is inspired by the organization of the early stages in the biological olfactory system, and proves to be highly suitable for high-dimensional feature vectors with small number of training samples. We demonstrate the method on experimental data from two metal oxide sensors driven by a sinusoidal temperature profile.
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