  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 (SMIDA) 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 inter-domain 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.
