摘要
提出一种自适应邻域选择算法,适用于所有基于局部的流形学习算法.该算法能够根据数据集分布的不同密度和曲率选择合适的邻域大小,同时结合局部多维尺度变换(LMDS),在合适的邻域下直接降维并通过全局整合得到数据集的低维坐标.实验表明该算法可较好恢复较复杂数据集的低维几何结构.
An automatic neighborhood selection algorithm for manifold learning is proposed. It is suitable for all the manifold learning algorithms which need select the neighbors to get locally linear information. Through the algorithm, the proper neighborhood size of a dataset can be determined even under different data density and curvature. By adopting this method, the locally multidimensional scaling can reduce the dimensionality of data based on the suitable neighborhood, and the low-dimensional representations of the data can be get through global alignment. The experiment shows the algorithm can recover the sophisticated geometry structure of the data sets.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2008年第3期406-409,共4页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.60495019)
教育部博士点专项基金(No.20060247039)资助项目
关键词
流形学习
非线性降维
局部多维尺度变换(LMDS)
Manifold Learning, Nonlinear Dimensionality Reduction, Locally Multidimensional Scaling （LMDS）
