Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift

Understand

The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on.

• To mitigate this domain shift problem, domain adaptation (DA) techniques search for an optimal transformation that converts the (current) input data from a source domain to a target domain to learn a domain-invariant representation that reduces domain discrepancy.
• This paper proposes a novel supervised DA based on two steps.
• First, we search for an optimal class-dependent transformation from the source to the target domain from a few samples.