IEEE Trans Image Process. 2015 Oct;24(10):2941-54. doi: 10.1109/TIP.2015.2431440. Epub 2015 May 8.
Data-driven dictionaries have produced the state-of-the-art results in various classification tasks. However, when the target data has a different distribution than the source data, the learned sparse representation may not be optimal. In this paper, we investigate if it is possible to optimally represent both source and target by a common dictionary. In particular, we describe a technique which jointly learns projections of data in the two domains, and a latent dictionary which can succinctly represent both the domains in the projected low-dimensional space. The algorithm is modified to learn a common discriminative dictionary, which can further improve the classification performance. The algorithm is also effective for adaptation across multiple domains and is extensible to nonlinear feature spaces. The proposed approach does not require any explicit correspondences between the source and target domains, and yields good results even when there are only a few labels available in the target domain. We also extend it to unsupervised adaptation in cases where the same feature is extracted across all domains. Further, it can also be used for heterogeneous domain adaptation, where different features are extracted for different domains. Various recognition experiments show that the proposed method performs on par or better than competitive state-of-the-art methods.
数据驱动的字典在各种分类任务中取得了最先进的成果。然而，当目标数据的分布与源数据不同时，学习到的稀疏表示可能不是最优的。在本文中，我们研究了是否有可能通过一个共同的字典来最优地表示源和目标。具体来说，我们描述了一种技术，该技术可以联合学习两个域中的数据投影，并学习一个潜在的字典，该字典可以简洁地表示投影到低维空间中的两个域。该算法经过修改，可以学习一个共同的判别字典，从而进一步提高分类性能。该算法对于跨多个域的自适应也很有效，并且可以扩展到非线性特征空间。所提出的方法不需要源域和目标域之间的任何显式对应关系，即使在目标域中只有几个标签可用，也能得到很好的结果。我们还将其扩展到了跨所有域提取相同特征的无监督自适应情况。此外，它还可用于异构域自适应，其中不同的域提取不同的特征。各种识别实验表明，所提出的方法的性能与竞争的最先进方法相当或更好。
