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Machine Olfaction, Signal Processing and Pattern Recognition

[Odor Recognition Across Multiple E-Nose Systems]

Paper:

Lei Zhang*, Yan Liu, and Pingling Deng, Odor Recognition in Multiple E-Nose Systems With Cross-Domain Discriminative Subspace Learning, IEEE Transactions on Instrumentation and Measurement, vol. 66, no. 7, pp. 1679-1692, 2017. [paper]

Abstract:

In this paper, we propose an odor recognition framework for multiple electronic noses (E-noses), machine olfaction odor perception systems. Straight to the point, the proposed transferring odor recognition model is called crossdomain discriminative subspace learning (CDSL). General odor recognition problems with E-nose are single domain oriented, that is, recognition algorithms are often modeled and tested on the same one domain data set (i.e., from only one E-nose system). Different from that, we focus on a more realistic scenario: the recognition model is trained on a prepared source domain data set from a master E-nose system A, but tested on another target domain data set from a slave system B or C with the same type of the master system A. The internal device parameter variance between master and slave systems often results in data distribution discrepancy between source domain and target domain, such that single-domain-based odor recognition model may not be adapted to another domain. Therefore, we propose domain-adaptation-based odor recognition for addressing the realistic recognition scenario across systems. Specifically, the proposed CDSL method consists of three merits: 1) an intraclass scatter minimization- and an interclass scatter maximization-based discriminative subspace learning is solved on source domain; 2) a data fidelity and preservation constraint of the subspace is imposed on target domain without distortion; and 3) a minipatch feature weighted domain distance is minimized for closely connecting the source and target domains. Experiments and comparisons on odor recognition tasks in multiple E-noses demonstrate the efficiency of the proposed method.

Approach:

Schematic of the proposed CDSL method; after a subspace projection P, the source domain and target domain of different space distributions lie in a latent subspace with good distribution consistency (the centers of both domains become very close, and drift is removed); in this latent subspace, the classification of two classes is successfully achieved. Formally, the top coordinate system denotes the raw data points of source domain and target domain in 3-D. We use the word “center” to represent the mean of the features. From the top figure, we can see that the difference between the mean of source domain and the mean of target domain is large in each dimension.

Data:

Experiments:

References:

[1] A. Vergara, S. Vembu, T. Ayhan, M. A. Ryan, M. L. Homer, and R. Huerta, “Chemical gas sensor drift compensation using classifier ensembles,” Sens. Actuators B, Chem., vols. 166–167, pp. 320–329, May 2012.

[2] R. Gopalan, R. Li, and R. Chellappa, “Domain adaptation for object recognition: An unsupervised approach,” in Proc. ICCV, 2011, pp. 999–1006.

[3] B. Gong, Y. Shi, F. Sha, and K. Grauman, “Geodesic flow kernel for unsupervised domain adaptation,” in Proc. CVPR, 2012, pp. 2066–2073.

[4] K. Yan and D. Zhang, “Correcting instrumental variation and timevarying drift: A transfer learning approach with autoencoders,” IEEE Trans. Instrum. Meas., vol. 65, no. 9, pp. 2012–2022, Sep. 2016.