Domain adaptation with geometrical preservation and distribution alignment

Abstract

Domain adaptation aims to learn a robust classifier for the target domain by leveraging knowledge from a different source domain. Existing methods realize the alignment of cross-domain distributions in manifold subspace to reduce the distribution divergence between the two domains. However, there exists a conspicuous deficiency in them, i.e., the exploration of preserving statistical and geometrical properties simultaneously is still under insufficient, which, to some extent, would cause the under adaptation effect. The statistical and geometrical properties play an important role in minimizing the domain discrepancy underlying the joint probability distributions. For better and adequately exploiting the statistical and geometrical properties, we propose a novel feature adaptation method in this paper, called domain adaptation with geometrical preservation and distribution alignment (GPDA). Specifically, GPDA performs graph dual regularization in the nonnegative matrix factorization framework with label constraints, to learn the discriminative and domain-invariant features while preserving both the statistical properties and geometrical structures of the original data, such that the cross-domain difference can be effectively and positively narrowed. Meanwhile, GPDA simultaneously aligns the marginal and conditional probability distributions in the nonnegative matrix factorization framework during the learning of domain-invariant features, to further minimize the domain gap between the source and target domains, which can adequately transfer knowledge from the source domain to the target domain. Extensive experiments on seven benchmark datasets demonstrate the effectiveness of the proposed GPDA algorithm in cross-domain image classification.