From Shared Subspaces to Shared Landmarks: A Robust Multi-Source Classification Approach

Abstract

Training machine leaning algorithms on augmented data from different related sources is a challenging task. This problem arises in several applications, such as the Internet of Things (IoT), where data may be collected from devices with different settings. The learned model on such datasets can generalize poorly due to distribution bias. In this paper we consider the problem of classifying unseen datasets, given several labeled training samples drawn from similar distributions. We exploit the intrinsic structure of samples in a latent subspace and identify landmarks, a subset of training instances from different sources that should be similar. Incorporating subspace learning and landmark sel..