Recently, transfer subspace learning based approaches have shown to be a valid alternative to unsupervised subspace clustering and temporal data clustering for human motion segmentation (HMS). These approaches leverage prior knowledge from a source domain to improve clustering performance on a target domain, and currently they represent the state of the art in HMS. Bucking this trend, in this paper, we propose a novel unsupervised model that learns a representation of the data and digs clustering information from the data itself. Our model is reminiscent of temporal subspace clustering, but presents two critical differences. First, we learn an auxiliary data matrix that can deviate from the initial data, hence confer more degrees of freedom to the coding matrix. Second, we introduce a regularization term for this auxiliary data matrix that preserves the local geometrical structure present in the high-dimensional space. The proposed model is efficiently optimized by using an original Alternating Direction Method of Multipliers (ADMM) formulation allowing to learn jointly the auxiliary data representation, a nonnegative dictionary and a coding matrix. Experimental results on four benchmark datasets for HMS demonstrate that our approach achieves significantly better clustering performance then state-of-the-art methods, including both unsupervised and more recent semi-supervised transfer learning approaches.
翻译:最近,基于子空间学习的转移子空间学习方法证明,是取代无人监督的子空间集群和用于人类运动分离的时空数据组合的有效替代方法。这些方法利用来源领域的先前知识来提高目标领域的群集性能,目前它们代表了HMS的先进状态。在本文中,我们提出了一个新的、未经监督的模型,从数据本身中学习数据的表示方式,并从数据本身中挖掘集成信息。我们的模型是时间子空间群集的记忆,但呈现两个关键差异。首先,我们学习了一个辅助数据矩阵,可以偏离初始数据,从而给编码矩阵带来更大程度的自由度。第二,我们为这一辅助数据矩阵引入了一个正规化的术语,以维护高空间中存在的本地几何结构。我们提出的模型通过使用最初的变换方向方法来有效优化,从而能够共同学习辅助数据表达方式、非否定式字典和随后的编码矩阵。四个基准数据集的实验结果显示,HMS的四套基准数据集中包括最近采用的方法,并大大改进了前置式组合方法。