IEEE Trans Pattern Anal Mach Intell. 2017 Jul;39(7):1414-1430. doi: 10.1109/TPAMI.2016.2599532. Epub 2016 Aug 11.
This paper addresses classification tasks on a particular target domain in which labeled training data are only available from source domains different from (but related to) the target. Two closely related frameworks, domain adaptation and domain generalization, are concerned with such tasks, where the only difference between those frameworks is the availability of the unlabeled target data: domain adaptation can leverage unlabeled target information, while domain generalization cannot. We propose Scatter Component Analyis (SCA), a fast representation learning algorithm that can be applied to both domain adaptation and domain generalization. SCA is based on a simple geometrical measure, i.e., scatter, which operates on reproducing kernel Hilbert space. SCA finds a representation that trades between maximizing the separability of classes, minimizing the mismatch between domains, and maximizing the separability of data; each of which is quantified through scatter. The optimization problem of SCA can be reduced to a generalized eigenvalue problem, which results in a fast and exact solution. Comprehensive experiments on benchmark cross-domain object recognition datasets verify that SCA performs much faster than several state-of-the-art algorithms and also provides state-of-the-art classification accuracy in both domain adaptation and domain generalization. We also show that scatter can be used to establish a theoretical generalization bound in the case of domain adaptation.
本文讨论了在特定目标领域的分类任务，其中标记的训练数据仅来自与目标不同（但相关）的源域。两个密切相关的框架，域自适应和域泛化，涉及到这样的任务，这些框架之间的唯一区别是目标的未标记数据的可用性：域自适应可以利用未标记的目标信息，而域泛化则不能。我们提出了分散分量分析（SCA），这是一种快速的表示学习算法，可应用于域自适应和域泛化。SCA 基于一个简单的几何度量，即散度，它在再生核希尔伯特空间上操作。SCA 找到了一种表示方法，在最大化类别的可分离性、最小化域之间的不匹配性和最大化数据的可分离性之间进行权衡；每一个都通过散度来量化。SCA 的优化问题可以简化为广义特征值问题，从而得到快速而精确的解。在基准跨域目标识别数据集上的综合实验验证了 SCA 比几种最先进的算法快得多，并且在域自适应和域泛化中都提供了最先进的分类精度。我们还表明，在域自适应的情况下，散度可以用于建立一个理论的泛化界限。
