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Article IEEE Transactions on Knowledge and Data Engineering ; Volume 28, Issue 8 , 2016 , Pages 2128-2141 ; 10414347 (ISSN) ; Soleymani Baghshah, M ; Gheisari, M ; Sharif University of Technology
IEEE Computer Society
Almost all of the existing domain adaptation methods assume that all test data belong to a single stationary target distribution. However, in many real world applications, data arrive sequentially and the data distribution is continuously evolving. In this paper, we tackle the problem of adaptation to a continuously evolving target domain that has been recently introduced. We assume that the available data for the source domain are labeled but the examples of the target domain can be unlabeled and arrive sequentially. Moreover, the distribution of the target domain can evolve continuously over time. We propose the Evolving Domain Adaptation (EDA) method that first finds a new feature space...