Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/79792
Title: Learning domain-invariant subspace using domain features and independence maximization
Authors: Yan, K
Kou, L 
Zhang, D 
Keywords: Dimensionality reduction
Domain adaptation
Drift correction
Hilbert-Schmidt independence criterion (HSIC)
Machine olfaction
Transfer learning
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on cybernetics, Jan. 2018, v. 48, no. 1, p. 288-299 How to cite?
Journal: IEEE transactions on cybernetics 
Abstract: Domain adaptation algorithms are useful when the distributions of the training and the test data are different. In this paper, we focus on the problem of instrumental variation and time-varying drift in the field of sensors and measurement, which can be viewed as discrete and continuous distributional change in the feature space. We propose maximum independence domain adaptation (MIDA) and semi-supervised MIDA to address this problem. Domain features are first defined to describe the background information of a sample, such as the device label and acquisition time. Then, MIDA learns a subspace which has maximum independence with the domain features, so as to reduce the interdomain discrepancy in distributions. A feature augmentation strategy is also designed to project samples according to their backgrounds so as to improve the adaptation. The proposed algorithms are flexible and fast. Their effectiveness is verified by experiments on synthetic datasets and four real-world ones on sensors, measurement, and computer vision. They can greatly enhance the practicability of sensor systems, as well as extend the application scope of existing domain adaptation algorithms by uniformly handling different kinds of distributional change.
URI: http://hdl.handle.net/10397/79792
ISSN: 2168-2267
EISSN: 2168-2275
DOI: 10.1109/TCYB.2016.2633306
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