A Novel Dictionary Learning Method for Gas Identification With a Gas Sensor Array

Discriminative dictionary learning has been successfully applied in pattern recognition field. In most of dictionary learning methods, l0-norm or l1-norm is used to regularize the sparse representation coefficients, which makes the computing time consuming. In this paper, we present a novel dictionary learning method to improve the gas identification performance of the electronic nose. It has significantly less complexity but leads to very competitive classification results. An analysis dictionary is trained to generate discriminative code by a simple linear projection, while a synthesis dictionary is trained to obtain discriminative reconstruction. Moreover, class label information is utilized to promote the discriminative power of the coding coefficients. The analysis dictionary and synthesis dictionary are trained jointly by an iterative method, which makes the learned projection dictionaries better fit with each other so that the more effective gas identification can be obtained. The proposed algorithm is evaluated on the analysis of different concentration of carbon monoxide, methane, hydrogen, benzene, formaldehyde, ethylene, propane, and ethanol. Experimental results show that the proposed method is not only effective in the signal analysis, but also useful and applicable to the performance enhancement of the current electronic noses.