IEEE Trans Image Process. 2016 Feb;25(2):850-63. doi: 10.1109/TIP.2015.2510498. Epub 2015 Dec 18.
In this paper, we address the problem of unsupervised domain transfer learning in which no labels are available in the target domain. We use a transformation matrix to transfer both the source and target data to a common subspace, where each target sample can be represented by a combination of source samples such that the samples from different domains can be well interlaced. In this way, the discrepancy of the source and target domains is reduced. By imposing joint low-rank and sparse constraints on the reconstruction coefficient matrix, the global and local structures of data can be preserved. To enlarge the margins between different classes as much as possible and provide more freedom to diminish the discrepancy, a flexible linear classifier (projection) is obtained by learning a non-negative label relaxation matrix that allows the strict binary label matrix to relax into a slack variable matrix. Our method can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise. We formulate our problem as a constrained low-rankness and sparsity minimization problem and solve it by the inexact augmented Lagrange multiplier method. Extensive experiments on various visual domain adaptation tasks show the superiority of the proposed method over the state-of-the art methods. The MATLAB code of our method will be publicly available at http://www.yongxu.org/lunwen.html.
在本文中，我们解决了无监督领域迁移学习的问题，即在目标域中没有标签。我们使用变换矩阵将源数据和目标数据都转换到一个公共子空间中，其中每个目标样本可以表示为源样本的组合，使得来自不同领域的样本可以很好地交织。通过这种方式，减少了源域和目标域之间的差异。通过对重构系数矩阵施加联合低秩和稀疏约束，可以保留数据的全局和局部结构。为了尽可能扩大不同类之间的边界，并提供更多的自由度来减少差异，通过学习非负标签松弛矩阵获得了灵活的线性分类器（投影），允许严格的二进制标签矩阵松弛为松弛变量矩阵。我们的方法可以通过使用稀疏矩阵来对噪声建模来避免潜在的负迁移，因此对不同类型的噪声更稳健。我们将问题表述为一个受限的低秩和稀疏性最小化问题，并通过非精确增广拉格朗日乘子法求解。在各种视觉领域自适应任务上的广泛实验表明，该方法优于最先进的方法。我们方法的 MATLAB 代码将在 http://www.yongxu.org/lunwen.html 上公开。
