基于极限学习机参数迁移的域适应算法

针对含少量标签样本的迁移学习问题，本文提出了基于极限学习机（Extreme learning machine，ELM）参数迁移的域适应算法，其核心思想是将目标域的ELM分类器参数投影到源域参数空间中，使其最大限度地与源域的分类器参数分布相同.此外，考虑到迁移中有可能带来负迁移的情况，在目标函数中引入正则项约束.本文算法与以往的域适应算法相比优势在于，其分类器参数以及转移矩阵是同时优化得到的，并且其目标函数求解过程相对简单.实验结果表明，与主流的域适应算法相比，本文算法在精度与效率上都表现出明显的优势.

Domain Adaption Algorithm with ELM Parameter Transfer

In allusion to transfer learning problem with a small number of labeled samples, a domain adaption method through transferring extreme learning machine (ELM) parameters is proposed in this paper. The core idea is projecting the target ELM parameters on to the source and making the parameters maximally aligned with the source. In addition, considering the transformation may cause negative transfer, a regular term is added to the objective function. Unlike the existing domain adaption method, the parameters of classifier and the transformation matrix can be calculated simultaneously, and the objective function can be easily solved. Experiments demonstrate the proposed method has potential advantages in terms of accuracy and efficiency compared to the state-of-the-art approaches.