期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 44, 期 12, 页码 2264-2273出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2014.2305701
关键词
Domain adaptation; riemannian manifold; support vector machine
类别
资金
- National Natural Science Foundation of China [61125106, 61202297, 61300138]
- Shaanxi Key Innovation Team of Science and Technology [2012KCT-04]
- Natural Science Foundation of Fujian Province [2013J01239]
- Singapore MoE Tier 2 Project [ARC42/13]
Domain adaptation has shown promising results in computer vision applications. In this paper, we propose a new unsupervised domain adaptation method called domain adaptation by shifting covariance (DASC) for object recognition without requiring any labeled samples from the target domain. By characterizing samples from each domain as one covariance matrix, the source and target domain are represented into two distinct points residing on a Riemannian manifold. Along the geodesic constructed from the two points, we then interpolate some intermediate points (i.e., covariance matrices), which are used to bridge the two domains. By utilizing the principal components of each covariance matrix, samples from each domain are further projected into intermediate feature spaces, which finally leads to domain-invariant features after the concatenation of these features from intermediate points. In the multiple source domain adaptation task, we also need to effectively integrate different types of features between each pair of source and target domains. We additionally propose an SVM based method to simultaneously learn the optimal target classifier as well as the optimal weights for different source domains. Extensive experiments demonstrate the effectiveness of our method for both single source and multiple source domain adaptation tasks.
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