Abstract

Unsupervised transfer learning methods usually exploit the labeled source data to learn a classifier for unlabeled target data with a different but related distribution. However, most of the existing transfer learning methods leverage 0-1 matrix as labels which greatly narrows the flexibility of transfer learning. Another major limitation is that these methods are influenced by the redundant features and noises residing in cross-domain data. To cope with these two issues simultaneously, this paper proposes a redirected transfer learning (RTL) approach for unsupervised transfer learning with a multi-layer subspace learning structure. Specifically, in the first layer, we first learn a robust subspace where data from different domains can be well interlaced. This is made by reconstructing each target sample with the lowest-rank representation of source samples. Besides, imposing the \(L_{2,1}\) -norm sparsity on the regression term and regularization term brings robustness against noise and works for selecting informative features, respectively. In the second layer, we further introduce a redirected label strategy in which the strict binary labels are relaxed into continuous values for each datum. To handle effectively unknown labels of the target domain, we construct the pseudo-labels iteratively for unlabeled target samples to improve the discriminative ability in classification. The superiority of our method in classification tasks is confirmed on several cross-domain datasets.

中文翻译：

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用于稳健多层子空间学习的重定向迁移学习

摘要

无监督迁移学习方法通​​常利用标记的源数据来学习具有不同但相关分布的未标记目标数据的分类器。然而，现有的迁移学习方法大多利用0-1矩阵作为标签，这大大限制了迁移学习的灵活性。另一个主要限制是这些方法受到跨域数据中的冗余特征和噪声的影响。为了同时解决这两个问题，本文提出了一种具有多层子空间学习结构的重定向迁移学习（RTL）方法，用于无监督迁移学习。具体来说，在第一层中，我们首先学习一个鲁棒的子空间，其中来自不同域的数据可以很好地交错。这是通过使用源样本的最低等级表示重建每个目标样本来实现的。此外，对回归项和正则化项施加\(L_{2,1}\)范数稀疏性可以分别带来针对噪声的鲁棒性并有助于选择信息丰富的特征。在第二层中，我们进一步引入了重定向标签策略，其中严格的二进制标签被放宽为每个数据的连续值。为了有效处理目标域的未知标签，我们针对未标记的目标样本迭代构建伪标签，以提高分类的判别能力。我们的方法在分类任务中的优越性在几个跨域数据集上得到了证实。