Marginal Subspace Learning With Group Low-Rank for Unsupervised Domain Adaptation
Liran Yang, Qinghua Zhou, Bin Lu
Abstract
Unsupervised domain adaptation is intended to construct a reliable model for the unlabeled target samples using the well-labeled but differently distributed source samples. To tackle the domain shift issue, learning domain-invariant feature representations across domains is important, and most of the existing methods have concentrated on this goal. However, these methods rarely take into consideration the group discriminability of the feature representation, which is detrimental to the final recognition. Therefore, this article proposes a novel unsupervised domain adaptation method, named marginal subspace learning with group low-rank (MSL-GLR), to extract both domain-invariant and discriminative feature representations. Specifically, MSL-GLR uses the retargeting strategy to relax the regression matrix, such that the regression values would be forced to satisfy a margin maximization criterion for the requirement of correct classification. Moreover, MSL-GLR imposes a class-induced low-rank constraint, which enables the samples of each class to be located in their respective subspace. In this way, the distance between samples from the same class can be decreased and the discriminant ability of the projection is greatly improved. Furthermore, with the help of alternating direction method of multipliers (ADMM), an efficient algorithm is presented to solve the resulting optimization problem. Finally, the effectiveness of the proposed MSL-GLR is demonstrated by comprehensive evaluations on multiple domain adaptation benchmark datasets.