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Visual domain adaptation using weighted subspace alignment

Domain Adaptation (DA) has attracted a lot of attention in recent years. DA aims at overcoming the covariate shift in dataset and aligning multiple existing but partially related data collections. In this paper, we propose a new DA algorithm which aligns the weighted subspaces generated from source samples and target samples. The weighted subspaces of source samples are generated using weighted Principal Component Analysis (PCA). Specifically, the source samples closer to the target domain are given higher weights during the construction of subspaces, which is definitely beneficial for building an adaptable classifier. Subsequently, the weighted subspaces of source samples and the subspaces of target samples are aligned to achieve domain adaptation. Experimental results on standard datasets demonstrate the advantages of our approach over state-of-the-art DA approaches.