论文标题
基于本地和全球结构保存的光谱群集
Local and Global Structure Preservation Based Spectral Clustering
论文作者
论文摘要
光谱聚类（SC）广泛用于非线性歧管上的聚类数据。 SC的目的是通过考虑将局部邻域结构保存在多种数据数据上来聚集数据。本文将光谱聚类扩展到基于局部和全球结构保护的光谱聚类（LGPSC），该光谱聚类（LGPSC）同时融合了全球结构和局部邻域结构。对于此扩展，LGPSC提出了两个模型，以将局部结构保存扩展到局部和全局结构保护：光谱聚类指导的主成分分析模型和多级模型。最后，我们将最新方法的实验结果与我们的两个LGPSC模型在各种数据集上进行了比较，从而实验结果证实了我们的LGPSC模型对群集非线性数据的有效性。
Spectral Clustering (SC) is widely used for clustering data on a nonlinear manifold. SC aims to cluster data by considering the preservation of the local neighborhood structure on the manifold data. This paper extends Spectral Clustering to Local and Global Structure Preservation Based Spectral Clustering (LGPSC) that incorporates both global structure and local neighborhood structure simultaneously. For this extension, LGPSC proposes two models to extend local structures preservation to local and global structures preservation: Spectral clustering guided Principal component analysis model and Multilevel model. Finally, we compare the experimental results of the state-of-the-art methods with our two models of LGPSC on various data sets such that the experimental results confirm the effectiveness of our LGPSC models to cluster nonlinear data.
