上海科技大学知识管理系统

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 L2,1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$L_{2,1}$$\end{document}-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.