Subspace learning based transfer learning methods commonly find a common subspace where the discrepancy of the source and target domains is reduced. The final classification is also performed in such subspace. However, the minimum discrepancy does not guarantee the best classification performance and thus the common subspace may be not the best discriminative. In this paper, we propose a latent elastic-net transfer learning (LET) method by simultaneously learning a latent subspace and a discriminative subspace. Specifically, the data from different domains can be well interlaced in the latent subspace by minimizing Maximum Mean Discrepancy (MMD). Since the latent subspace decouples inputs and outputs and, thus a more compact data representation is obtained for discriminative subspace learning. Based on the latent subspace, we further propose a low-rank constraint based matrix elastic-net regression to learn another subspace in which the intrinsic intra-class structure correlations of data from different domains is well captured. In doing so, a better discriminative alignment is guaranteed and thus LET finally learns another discriminative subspace for classification. Experiments on visual domains adaptation tasks show the superiority of the proposed LET method.

中文翻译：

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潜在的弹性网转移学习。

基于子空间学习的转移学习方法通​​常会找到一个公共子空间，从而减少源域和目标域的差异。最终分类也在此类子空间中执行。但是，最小差异不能保证最佳分类性能，因此，公共子空间可能不是最佳区分。本文通过同时学习潜在子空间和判别子空间，提出了一种潜在的弹性网转移学习方法。具体而言，通过最小化最大平均差异（MMD），可以将来自不同域的数据很好地交织在潜在子空间中。由于潜在子空间将输入和输出解耦，因此获得了更紧凑的数据表示形式，用于判别子空间学习。根据潜在子空间，我们进一步提出了一种基于低秩约束的矩阵弹性网回归，以学习另一个子空间，其中可以很好地捕获来自不同域的数据的内在类内结构相关性。这样做可以确保更好的区分性对齐，因此LET最终将学习另一个区分子空间以进行分类。在视觉域自适应任务上的实验表明了所提出的LET方法的优越性。