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

Training machine leaning algorithms on augmented data fromdifferent related sources is a challenging task. This problemarises in several applications, such as the Internet of Things(IoT), where data may be collected from devices with differentsettings. The learned model on such datasets can generalizepoorly due to distribution bias. In this paper we considerthe problem of classifying unseen datasets, given several labeledtraining samples drawn from similar distributions. Weexploit the intrinsic structure of samples in a latent subspaceand identify landmarks, a subset of training instances fromdifferent sources that should be similar. Incorporating subspacelearning and landmark selection enhances generalizationby alleviating the impact of noise and outliers, as well asimproving efficiency by reducing the size of the data. However,since addressing the two issues simultaneously resultsin an intractable problem, we relax the objective functionby leveraging the theory of nonlinear projection and solve atractable convex optimisation. Through comprehensive analysis,we show that our proposed approach outperforms stateof-the-art results on several benchmark datasets, while keepingthe computational complexity low.