This paper introduces a learning scheme to construct a Hilbert space (i.e., a vector space along its inner product) to address both unsupervised and semi-supervised domain adaptation problems. This is achieved by learning projections from each domain to a latent space along the Mahalanobis metric of the latent space to simultaneously minimizing a notion of domain variance while maximizing a measure of discriminatory power. In particular, we make use of the Riemannian optimization techniques to match statistical properties (e.g., first and second order statistics) between samples projected into the latent space from different domains. Upon availability of class labels, we further deem samples sharing the same label to form more compact clusters while pulling away samples coming from different classes.We extensively evaluate and contrast our proposal against state-of-the-art methods for the task of visual domain adaptation using both handcrafted and deep-net features. Our experiments show that even with a simple nearest neighbor classifier, the proposed method can outperform several state-of-the-art methods benefitting from more involved classification schemes.
In this work, we propose a semisupervised extension to a well-known supervised domain adaptation approach (EA) (Daume III, 2007). Our proposed approach (EA++) builds on the notion of augmented space (introduced in EA) and harnesses unlabeled data in target domain to ameliorate the transfer of information from source to target. This semisupervised approach to domain adaptation is extremely simple to implement, and can be applied as a pre-processing step to any supervised learner. Experimental results on sequential labeling tasks demonstrate the efficacy of the proposed method.