IEEE Trans Cybern. 2019 May;49(5):1909-1922. doi: 10.1109/TCYB.2018.2816981. Epub 2018 Apr 10.
Extreme learning machine (ELM) has been applied in a wide range of classification and regression problems due to its high accuracy and efficiency. However, ELM can only deal with cases where training and testing data are from identical distribution, while in real world situations, this assumption is often violated. As a result, ELM performs poorly in domain adaptation problems, in which the training data (source domain) and testing data (target domain) are differently distributed but somehow related. In this paper, an ELM-based space learning algorithm, domain space transfer ELM (DST-ELM), is developed to deal with unsupervised domain adaptation problems. To be specific, through DST-ELM, the source and target data are reconstructed in a domain invariant space with target data labels unavailable. Two goals are achieved simultaneously. One is that, the target data are input into an ELM-based feature space learning network, and the output is supposed to approximate the input such that the target domain structural knowledge and the intrinsic discriminative information can be preserved as much as possible. The other one is that, the source data are projected into the same space as the target data and the distribution distance between the two domains is minimized in the space. This unsupervised feature transformation network is followed by an adaptive ELM classifier which is trained from the transferred labeled source samples, and is used for target data label prediction. Moreover, the ELMs in the proposed method, including both the space learning ELM and the classifier, require just a small number of hidden nodes, thus maintaining low computation complexity. Extensive experiments on real-world image and text datasets are conducted and verify that our approach outperforms several existing domain adaptation methods in terms of accuracy while maintaining high efficiency.
极限学习机（ELM）由于其高精度和高效率，已被广泛应用于分类和回归问题。然而，ELM 只能处理训练数据和测试数据来自同一分布的情况，而在实际情况中，这种假设往往是违反的。因此，ELM 在域自适应问题中表现不佳，在这些问题中，训练数据（源域）和测试数据（目标域）分布不同，但存在某种关联。在本文中，提出了一种基于 ELM 的空间学习算法，即域空间转移 ELM（DST-ELM），用于处理无监督域自适应问题。具体来说，通过 DST-ELM，在没有目标数据标签的情况下，在域不变空间中对源域和目标域数据进行重构。同时实现两个目标。一个是将目标数据输入到基于 ELM 的特征空间学习网络中，输出应尽可能接近输入，从而尽可能保留目标域的结构知识和内在判别信息。另一个是将源数据投影到与目标数据相同的空间中，并在该空间中最小化两个域之间的分布距离。这个无监督特征转换网络后面跟着一个自适应 ELM 分类器，该分类器是从转移的有标签源样本中训练得到的，用于目标数据标签预测。此外，所提出的方法中的 ELM，包括空间学习 ELM 和分类器，只需要少量的隐藏节点，从而保持低计算复杂度。在真实的图像和文本数据集上进行了广泛的实验，结果表明，我们的方法在保持高效率的同时，在准确性方面优于几种现有的域自适应方法。
