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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Semi-supervised Domain Adaptation on Manifolds
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Semi-supervised Domain Adaptation on Manifolds

机译：流形上的半监督域自适应

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摘要

In real-life problems, the following semi-supervised domain adaptation scenario is often encountered: we have full access to some source data, which is usually very large; the target data distribution is under certain unknown transformation of the source data distribution; meanwhile, only a small fraction of the target instances come with labels. The goal is to learn a prediction model by incorporating information from the source domain that is able to generalize well on the target test instances. We consider an explicit form of transformation functions and especially linear transformations that maps examples from the source to the target domain, and we argue that by proper preprocessing of the data from both source and target domains, the feasible transformation functions can be characterized by a set of rotation matrices. This naturally leads to an optimization formulation under the special orthogonal group constraints. We present an iterative coordinate descent solver that is able to jointly learn the transformation as well as the model parameters, while the geodesic update ensures the manifold constraints are always satisfied. Our framework is sufficiently general to work with a variety of loss functions and prediction problems. Empirical evaluations on synthetic and real-world experiments demonstrate the competitive performance of our method with respect to the state-of-the-art.
机译：在现实生活中，经常会遇到以下半监督域适应情况：我们可以完全访问某些通常非常大的源数据；目标数据分布处于源数据分布的某些未知变换之下；同时，只有一小部分目标实例带有标签。目标是通过合并来自源域的信息来学习预测模型，该信息能够很好地概括目标测试实例。我们考虑了转换函数的显式形式，尤其是线性转换，这种转换将示例从源域映射到目标域，并且我们认为，通过对源域和目标域的数据进行适当的预处理，可行的转换函数可以用一组特征来表征。旋转矩阵。这自然会导致在特殊正交组约束下的优化公式。我们提出了一种迭代坐标下降求解器，它能够共同学习变换以及模型参数，而测地线更新可确保始终满足流形约束。我们的框架足够通用，可以处理各种损失函数和预测问题。对合成和现实世界实验的经验评估表明，我们的方法相对于最新技术具有竞争优势。

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