A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and k-Nearest Neighbor Graph. (20th February 2017)

• Record Type: Journal Article
• Authors: Zu, Baokai; Xia, Kewen; Pan, Yongke; Niu, Wenjia
• Other Names: Graña Manuel Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2017(2017)
• Publication Date: 2017-02-20
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2017/9290230
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : 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 andk -nearest neighbor (LRKNN) graph. In our LRKNN graph, we map the data to the LR feature space and then thek N N is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and thek -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.