This article presents a novel discriminative subspace-learning-based unsupervised domain
adaptation (DA) method for the gas sensor drift problem. Many existing subspace learning …

Subspace learning is a popular machine learning method that has been frequently applied
for gas sensor calibration; however, there are the following limitations in the latent subspace …

Sensor drift is one of the severe issues that gas sensors suffer from. To alleviate the sensor
drift problem, a gas sensor drift compensation approach is proposed based on local …

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 …

In sensor-related subjects, sensor drift is an urgent and challenging problem because of its
negative impact on the recognition performance and long-term detection of sensors. Earlier …

Abstract Domain adaptation (DA) is one of the most promising techniques for leveraging
existing knowledge from a source domain and applying it to a related target domain. Most …

In traditional machine learning algorithms, the classification models are learned on the
training data (source domain) to reuse for labelling the test data (target domain) where the …

In recent years, domain adaptation (DA) method has been proposed to solve the problem of
domain shift between the training set and test set. However, most feature-based …

Sensor drift is a long-existing unpredictable problem that deteriorates the performance of
gaseous substance recognition, calling for an antidrift domain adaptation algorithm …

A prevailing problem in many machine learning tasks is that the training (ie, source domain)
and test data (ie, target domain) have different distribution [ie, non-independent identical …