ADAPTIVE NEIGHBORHOOD SELECTION FOR MANIFOLD LEARNING

摘要：

As a class of nonlinear dimensionality reduction methods, manifold learning can effectively construct nonlinear low dimensional manifolds from sampled data points embedded in high dimensional spaces. However, the results of most manifold learning algorithms are extremely sensitive to the parameters which control the selection of neighbors at each point. In this paper, an adaptive neighborhood selection method was proposed. Through ranking on manifold to select candidate neighborhood, and then estimating local tangent space, we can select the neighborhood of each point adaptively. Experimental results on several synthetic and real datasets demonstrate the effectiveness of our method.

关键词： Manifold Learning Manifold ranking Local tangent space Adaptive neighborhood selection

作者: JIA WEI HONG PENG YI-SHEN LIN ZHI-MAO HUANG JIA-BING WANG

作者单位: School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China

会议类型: 国际会议

会议名称: 2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)

会议地点: 昆明

会议语种:英文

页码: 380-384

在线出版日期: 2008-07-12（万方平台首次上网日期，不代表论文的发表时间）