2 research outputs found
Domain Adaptation Extreme Learning Machines for Drift Compensation in E-nose Systems
This paper addresses an important issue, known as sensor drift that behaves a
nonlinear dynamic property in electronic nose (E-nose), from the viewpoint of
machine learning. Traditional methods for drift compensation are laborious and
costly due to the frequent acquisition and labeling process for gases 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, semi-supervised
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, referred to as 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 gases recognition in E-nose systems, without loss of
the computational efficiency and learning ability of traditional ELM. In the
unified framework, two algorithms called DAELM-S and DAELM-T are proposed for
the purpose of this paper, respectively. In order to percept the differences
among ELM, DAELM-S and DAELM-T, two remarks are provided. Experiments on the
popular sensor drift data with multiple batches collected by 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.Comment: 11 pages, 9 figures, to appear in IEEE Transactions on
Instrumentation and Measuremen
Parsimonious Wavelet Kernel Extreme Learning Machine
In this study, a parsimonious scheme for wavelet kernel extreme learning machine (named PWKELM) was introduced by
combining wavelet theory and a parsimonious algorithm into kernel extreme learning machine (KELM). In the wavelet
analysis, bases that were localized in time and frequency to represent various signals effectively were used. Wavelet
kernel extreme learning machine (WELM) maximized its capability to capture the essential features in “frequency-rich”
signals. The proposed parsimonious algorithm also incorporated significant wavelet kernel functions via iteration in
virtue of Householder matrix, thus producing a sparse solution that eased the computational burden and improved
numerical stability. The experimental results achieved from the synthetic dataset and a gas furnace instance demonstrated
that the proposed PWKELM is efficient and feasible in terms of improving generalization accuracy and real time
performance