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Machine Olfaction, Signal Processing and Pattern Recognition

[Semi-supervised Learning in Electronic Nose]

Paper:

Lei Zhang*, David Zhang, Xin Yin, and Yan Liu, A Novel Semi-Supervised Learning Approach in Artificial Olfaction for E-Nose Application, IEEE Sensors Journal, vol. 16, no. 12, pp. 4919-4931, 2016. [paper]

Abstract:

Artificial olfaction data are usually represented by a sensor array embedded in an electronic nose system (E-Nose), such that each observation can be expressed as a feature vector for pattern recognition. The concerns of this paper are threefold: 1) each feature can be represented by multiple different modalities; 2) manual labeling of sensory data in real application is difficult and hardly impossible, which results in an issue of insufficient labeled data; and 3) classifier learning is generally independent of feature engineering, such that the recognition capability of E-Nose is restricted due to the unilateral suboptimum. Motivated by these concerns, in this paper, from a new perspective of multi-task learning, we aim at proposing a unified semi-supervised learning framework nominated as MFKS, and the merits are composed of three points. First, a multi-feature joint classifier learning with low-rank constraint is developed for exploiting the structural information of multiple feature modalities. The relatedness of sub-classifiers with respect to feature modalities is preserved by imposing a low-rank constraint on the group classifier. Second, with a manifold assumption, a Laplacian graph manifold regularization is incorporated for capturing the intrinsic geometry of unlabeled data. Third, the features and classifiers are learned simultaneously in a unified framework, such that the optimality and robustness are improved. Experiments on two data sets, including large-scale 16-sensor data with 36-month drift and small-scale temperature modulated sensory data, demonstrate that the proposed approach has 4% improvement in classification accuracy than others.

Approach:

Framework

Multi-feature construction

Experimental Results

References:

[1] D. Zhou, O. Bousquet, T. Navin Lal, J. Weston, and B. Schölkopf, “Learning with local and global consistency,” in Proc. NIPS, 2004, pp. 321–328.

[2] Y. Luo, D. Tao, B. Geng, C. Xu, and S. J. Maybank, “Manifold regularized multitask learning for semi-supervised multilabel image classification,” IEEE Trans. Image Process., vol. 22, no. 2, pp. 523–536, Feb. 2013.

[3] M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput., vol. 15, no. 6, pp. 1373–1396, 2003.

[4] S. Yan, D. Xu, B. Zhang, H.-J. Zhang, Q. Yang, and S. Lin, “Graph embedding and extensions: A general framework for dimensionality reduction,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 1, pp. 40–51, Jan. 2007.