Zu Baokai, Xia Kewen, Pan Yongke, Niu Wenjia
School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China; Key Lab of Big Data Computation of Hebei Province, Tianjin 300401, China; Computer Science Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA.
School of Electronic and Information Engineering, Hebei University of Technology, Tianjin 300401, China; Key Lab of Big Data Computation of Hebei Province, Tianjin 300401, China.
Comput Intell Neurosci. 2017;2017:9290230. doi: 10.1155/2017/9290230. Epub 2017 Feb 20.
Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and -nearest neighbor (LRKNN) graph. In our LRKNN graph, we map the data to the LR feature space and then the NN is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and the -nearest neighbor algorithm can maximally preserve the local geometrical structure of the data, the LRKNN graph can significantly improve the performance of SDA. Extensive experiments on several real-world databases show that the proposed LRKNN graph is an efficient graph constructor, which can largely outperform other commonly used baselines.
半监督判别分析（SDA）是一种半监督降维算法，它能够轻松解决样本外问题。相关工作通常聚焦于数据点不明显的几何关系，以提升SDA的性能。与这些相关工作不同，本文研究了正则化图构造，这在基于图的半监督学习方法中很重要。在本文中，我们提出了一种用于半监督判别分析的新型图，称为联合低秩和最近邻（LRKNN）图。在我们的LRKNN图中，我们将数据映射到LR特征空间，然后采用最近邻来满足SDA的算法要求。由于低秩表示可以捕获全局结构，而最近邻算法可以最大程度地保留数据的局部几何结构，因此LRKNN图可以显著提高SDA的性能。在多个真实世界数据库上进行的大量实验表明，所提出的LRKNN图是一种高效的图构造器，它在很大程度上优于其他常用的基线方法。
