关键词: 完全无监督域适应（WUDA）, 参数公共字典, 稀疏表示, 无标记小样本问题, 软大间隔聚类（SLMC）

Abstract: Unsupervised domain adaptation (UDA) concerns on the learning problem in which the source domain contains labeled samples and target domain only contains unlabeled samples. Its goal is to use the “knowledge”learnt from source domain with a large number of labeled samples to promote learning performance in the target domain where all the samples are unlabeled. In reality, however, there often exists the situation in which the samples are also unlabeled in the source domain, leading to the so-called whole unsupervised domain adaptation. This problem brings severe challenge to domain adaptation learning. To address this, inspired by soft large margin clustering (SLMC) which is proposed previously, this paper proposes a parameter-transfer method, i.e., whole unsupervised domain adaptation using sparse representation of parameter dictionary (WUDA). Specifically, borrowing the idea of SLMC that conducts data clustering in the output (label) space, WUDA realizes the knowledge transfer from the perspective of common dictionary of parameter (weight matrix of decision function) and adaptively learns the parameter dictionary between the weights of source domain and target domain. In addition, a [l2,1] norm regularization term is introduced to constrain the coefficient matrix of dictionary, which makes the domain weight adaptively selectable from the common dictionary. Finally, the experimental results on the related datasets show the significant improvement of WUDA on the clustering performance.

Key words: whole unsupervised domain adaptation (WUDA), common dictionary of parameter, sparse representation, problem of unlabeled small sample, soft large margin clustering (SLMC)