Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14761
Title: Domain Adaptation Extreme Learning Machines for Drift Compensation in E-Nose Systems
Authors: Zhang, L 
Zhang, D 
Keywords: Domain adaptation (DA)
Drift compensation
Electronic nose (E-nose)
Extreme learning machine (ELM)
Transfer learning
Issue Date: 2015
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on instrumentation and measurement, 2015, v. 64, no. 7, p. 1790-1801 How to cite?
Journal: IEEE transactions on instrumentation and measurement 
Abstract: This paper addresses an important issue known as sensor drift, which exhibits a nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of machine learning. Traditional methods for drift compensation are laborious and costly owing to the frequent acquisition and labeling process for gas samples' recalibration. Extreme learning machines (ELMs) have been confirmed to be efficient and effective learning techniques for pattern recognition and regression. However, ELMs primarily focus on the supervised, semisupervised, and unsupervised learning problems in single domain (i.e., source domain). To our best knowledge, ELM with cross-domain learning capability has never been studied. This paper proposes a unified framework called domain adaptation extreme learning machine (DAELM), which learns a robust classifier by leveraging a limited number of labeled data from target domain for drift compensation as well as gas recognition in E-nose systems, without losing the computational efficiency and learning ability of traditional ELM. In the unified framework, two algorithms called source DAELM (DAELM-S) and target DAELM (DAELM-T) are proposed in this paper. In order to perceive the differences among ELM, DAELM-S, and DAELM-T, two remarks are provided. Experiments on the popular sensor drift data with multiple batches collected using E-nose system clearly demonstrate that the proposed DAELM significantly outperforms existing drift-compensation methods without cumbersome measures, and also bring new perspectives for ELM.
URI: http://hdl.handle.net/10397/14761
ISSN: 0018-9456
EISSN: 1557-9662
DOI: 10.1109/TIM.2014.2367775
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