Abstract
Distribution mismatch caused by various resolutions, backgrounds, etc. can be easily found in multi-sensor systems. Domain adaptation attempts to reduce such domain discrepancy by means of different measurements, e.g., maximum mean discrepancy (MMD). Despite their success, such methods often fail to guarantee the separability of learned representation. To tackle this issue, we put forward a novel approach to jointly learn both domain-shared and discriminative representations. Specifically, we model the feature discrimination explicitly for two domains. Alternating discriminant optimization is proposed to obtain discriminative features with an l2 constraint in labeled source domain and sparse filtering is introduced to capture the intrinsic structures exists in the unlabeled target domain. Finally, they are integrated in a unified framework along with MMD to align domains. Extensive experiments compared with state-of-the-art methods verify the effectiveness of our method on cross-domain tasks.
Highlights
A basic assumption of many machine learning algorithms is that the training and testing data share the same distribution
We focus on unsupervised domain adaptation, which means that the target domain does not have any labeled samples
We focus on unsupervised domain adaptation which means that target domain has no labels at all
Summary
A basic assumption of many machine learning algorithms is that the training and testing data share the same distribution. An intuitive idea for domain adaptation is to re-weight the training samples and reduce the distance between the source and target domains at the instance level [7]. Another popular way is to reduce the discrepancy between domains at the feature level, which attempts to learn domain-shared representations. Ben et al pointed out that the transferable features can be obtained by minimizing the distance of domains and maximizing the source margin simultaneously [8] Based on this theory, many feature-driven domain adaptation methods have been proposed. Pan et al mapped the data from both domains to high-dimensional Hilbert space and minimized the domain discrepancy [9]
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