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Sensors 2017, 17(10), 2279; doi:10.3390/s17102279

Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection

College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China
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Received: 18 August 2017 / Revised: 29 September 2017 / Accepted: 30 September 2017 / Published: 7 October 2017
(This article belongs to the Special Issue Electronic Tongues and Electronic Noses)
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Abstract

For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning combined with sparse autoencoder and radial basis function (RBF) into the field. Self-taught learning is a kind of transfer learning that can transfer knowledge from other fields to target fields, can solve such problems that labeled data (target fields) and unlabeled data (other fields) do not share the same class labels, even if they are from entirely different distribution. In our paper, we obtain numerous cheap unlabeled pollutant gas samples (benzene, formaldehyde, acetone and ethylalcohol); however, labeled wound infection samples are hard to gain. Thus, we pose self-taught learning to utilize these gas samples, obtaining a basis vector θ. Then, using the basis vector θ, we reconstruct the new representation of wound infection samples under sparsity constraint, which is the input of classifiers. We compare RBF with partial least squares discriminant analysis (PLSDA), and reach a conclusion that the performance of RBF is superior to others. We also change the dimension of our data set and the quantity of unlabeled data to search the input matrix that produces the highest accuracy. View Full-Text
Keywords: electronic nose; self-taught learning; sparse autoencoder; wound infection electronic nose; self-taught learning; sparse autoencoder; wound infection
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He, P.; Jia, P.; Qiao, S.; Duan, S. Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection. Sensors 2017, 17, 2279.

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