AN INCREMENTAL ALGORITHM BASED ON K NEAREST NEIGHBOR PROJECTION FOR NONLINEAR DIMENSIONALITY REDUCTION

摘要：

Recently, there are several algorithms to perform dimensionality reduction on low-dimensional nonlinear manifolds embedded in a high-dimensional space, such as ISOMAP, LLE, Laplacian eigenmaps, SPE and so on. Most of these techniques work in batch mode. In this paper, we present an incremental nonlinear dimensionality reduction algorithm based on the k nearest neighbor projection. The method can effectively map new data into the low-dimensional space by building a locally linear transformation model between the original space and the embedded space. Moreover, the algorithm can treat data set with noise. Experiments show that the algorithm proposed is effective and robust.

关键词： Nonlinear dimensionality reduction manifold incremental algorithm k nearest neighbor projection

作者: LU-KUI SHI JIAN-WEI LI QING WU PI-LIAN HE YU-QING PENG

作者单位: School of Computer Science and Engineering, Hebei University of Technology, Tianjin 300130, China Department of Computer Science and Technology, Tianjin University, Tianjin 300072, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 1417-1421

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）