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

作     者：He, Peilin Jia, Pengfei Qiao, Siqi Duan, Shukai 

作者机构：Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China 

出 版 物：《SENSORS》 (Sensors)

年 卷 期：2017年第17卷第10期

页      面：2279页

核心收录：

学科分类：0710[理学-生物学] 071010[理学-生物化学与分子生物学] 0808[工学-电气工程] 07[理学] 0804[工学-仪器科学与技术] 0703[理学-化学] 

基　　金：Program for New Century Excellent Talents in University National Science Foundation of China [61372139, 61101233, 60972155] China Postdoctoral Science Foundation [2016M602630] Fundamental Research Funds for the Cetral Universities [XDJK2015C073] 

主　　题：electronic nose self-taught learning sparse autoencoder wound infection 

摘      要：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 theta. Then, using the basis vector theta, 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.