Zhou Nan, Cheng Hong, Pedrycz Witold, Zhang Yong, Liu Huaping
Center for Robotics, School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China.
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6R 2V4, Canada; Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia; Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland.
ISA Trans. 2016 Mar;61:104-118. doi: 10.1016/j.isatra.2015.12.011. Epub 2016 Jan 20.
In order to efficiently use the intrinsic data information, in this study a Discriminative Sparse Subspace Learning (DSSL) model has been investigated for unsupervised feature selection. First, the feature selection problem is formulated as a subspace learning problem. In order to efficiently learn the discriminative subspace, we investigate the discriminative information in the subspace learning process. Second, a two-step TDSSL algorithm and a joint modeling JDSSL algorithm are developed to incorporate the clusters׳ assignment as the discriminative information. Then, a convergence analysis of these two algorithms is provided. A kernelized discriminative sparse subspace learning (KDSSL) method is proposed to handle the nonlinear subspace learning problem. Finally, extensive experiments are conducted on real-world datasets to show the superiority of the proposed approaches over several state-of-the-art approaches.
为了有效地利用内在数据信息，本研究对一种判别式稀疏子空间学习（DSSL）模型进行了研究，用于无监督特征选择。首先，将特征选择问题表述为子空间学习问题。为了有效地学习判别性子空间，我们在子空间学习过程中研究判别信息。其次，开发了一种两步TDSSL算法和一种联合建模JDSSL算法，将聚类分配作为判别信息纳入其中。然后，对这两种算法进行了收敛性分析。提出了一种核判别式稀疏子空间学习（KDSSL）方法来处理非线性子空间学习问题。最后，在真实世界数据集上进行了广泛的实验，以展示所提出方法相对于几种现有最先进方法的优越性。
