Abnormal Odor Detection in Electronic Nose via Self-Expression Inspired Extreme Learning Machine

The electronic nose (E-nose), as a metal oxide semiconductor gas sensor system coupled with pattern recognition algorithms, is developed for approximating artificial olfaction functions. Ideal gas sensors should be with selectivity, reliability, and cross-sensitivity to different odors. However, a new problem is that abnormal odors (e.g., perfume, alcohol, etc.) would show strong sensor response, such that they deteriorate the usual usage of E-nose for target odor analysis. An intuitive idea is to recognize abnormal odors and remove them online. A known truth is that the kinds of abnormal odors are countless in real-world scenarios. Therefore, general pattern classification algorithms lose effect because it is expensive and unrealistic to obtain all kinds of abnormal odors data. In this paper, we propose two simple yet effective methods for abnormal odor (outlier) detection: 1) a self-expression model (SEM) with
$ {l_{1}/l_{2}}$
-norm regularizer is proposed, which is trained on target odor data for coding and then a very few abnormal odor data is used as prior knowledge for threshold learning and 2) inspired by self-expression mechanism, an extreme learning machine (ELM) based self-expression (SE
$^{ {2}}$
LM) is proposed, which inherits the advantages of ELM in solving a single hidden layer feed-forward neural network. Experiments on several datasets by an E-nose system fabricated in our laboratory prove that the proposed SEM and SE
$^{ {2}}$
LM methods are significantly effective for real-time abnormal odor detection.