Domain Adaptation with Randomized Expectation Maximization

Understand

Domain adaptation (DA) is the task of classifying an unlabeled dataset (target) using a labeled dataset (source) from a related domain.

• The majority of successful DA methods try to directly match the distributions of the source and target data by transforming the feature space.
• Despite their success, state of the art methods based on this approach are either involved or unable to directly scale to data with many features.
• This article shows that domain adaptation can be successfully performed by using a very simple randomized expectation maximization (EM) method.