Calibration transfer and drift compensation of e noses via coupled task learning

The problems of instrumental variation and sensor drift are receiving increasing concerns in the field of electronic noses. Because the two problems can be uniformly viewed as a variation of the data distribution in the feature space, they can be handled by algorithms such as transfer learning. In this paper, we propose a novel algorithm framework called transfer sample-based coupled task learning (TCTL). It is based on transfer learning and multi-task learning. Given labeled samples in the sour