Incremental Evolving Domain Adaptation
Incremental Evolving Domain Adaptation
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DOI：
10.1109/tkde.2016.2551241
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发表时间：
2016-08-01
影响因子：
8.9
通讯作者：
Gheisari, Marzieh
中科院分区：
文献类型：
--
作者：
Bitarafan, Adeleh;Baghshah, Mahdieh Soleymani;Gheisari, Marzieh
现有的领域自适应方法几乎都假设所有测试数据属于单一的平稳目标分布。然而，在许多真实的应用中，数据是顺序到达的，并且数据分布是不断演变的。在本文中，我们解决了适应一个不断发展的目标域，最近推出的问题。我们假设源域的可用数据是标记的，但目标域的示例可以是未标记的，并按顺序到达。此外，目标域的分布可以随着时间的推移而不断演变。我们提出了进化域自适应（EDA）方法，该方法首先找到一个新的特征空间，其中源域和当前目标域几乎无法区分。因此，源和目标域数据类似地分布在新的特征空间中，并且我们使用半监督分类方法来利用目标域的未标记数据和源域的标记数据。由于测试数据顺序到达，我们提出了一个增量的方法来寻找新的特征空间和半监督分类。在几个真实的数据集上的实验表明，我们提出的方法相比其他最近的方法的优越性。
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 in which the source domain and the current target domain are approximately indistinguishable. Therefore, source and target domain data are similarly distributed in the new feature space and we use a semi-supervised classification method to utilize both the unlabeled data of the target domain and the labeled data of the source domain. Since test data arrives sequentially, we propose an incremental approach both for finding the new feature space and for semi-supervised classification. Experiments on several real datasets demonstrate the superiority of our proposed method in comparison to the other recent methods.