点击次数：

论文类型：期刊论文

发表时间：2011-05-01

发表刊物：Journal of Information and Computational Science

收录刊物：EI、Scopus

卷号：8

期号：5

页面范围：725-737

ISSN号：15487741

摘要：Locally Linear Embedding (LLE) is an effective method for both single manifold embedding and multiple manifolds clustering. However, LLE may fail to model data drawn from mixture of manifolds because a point and its Nearest Neighbors (NNs) cannot guaranteed to be on the same manifold, especially when the point is at the junction of different manifolds. In this paper, we propose a new method, named Low Rank Embedding (LRE), to model the geometric structure of mixture of manifolds. LRE defines the neighborhood structure as linear combination of other samples with an additional minimum rank constraint on the points that belong to the same manifold. This way, LRE removes the dependency with the number and metric of NNs, and it makes a more accurate selection of the neighbors belonging to the same manifold. Both theoretical and experimental results show that LRE is a promising tool for embedding and clustering mixture of manifolds. Copyright ? 2011 Binary Information Press.