基于多样化流形学习的非线性矩阵分解数据聚类

NONLINEAR MATRIX FACTORIZATION DATA CLUSTERING BASED ON MANIFOLD LEARNING

为了捕获多方面数据中的局部几何结构,提升聚类性能,提出一种基于多样化流形学习的非线性矩阵分解数据聚类方法。为每一种相互关系构造一个P近邻图,以捕获两种不同类型的密切相关对象,从而准确地学习在数据的内部关系和内部关系上产生的多个流形,并在用非线性矩阵分解映射到新的低维数据空间时稳定地保持所学习的多样流形。多个数据集聚类结果表明该方法能够充分挖掘各种相关类型的部分表示,在精度和效率上均具备一定优势。

In order to capture the local geometric structure of multi-faceted data and improve the clustering performance,a nonlinear matrix factorization data clustering method based on manifold learning is proposed.A P-nearest neighbor graph was constructed for each relationship to capture two different types of closely related objects,so as to accurately learn the internal relations and multiple manifolds generated by the internal relations of data.And we stably kept the learned manifold when mapping to a new low dimensional data space with nonlinear matrix factorization.The clustering results of multiple data sets show that the method can fully mine the partial representation of various related types,and has certain advantages in accuracy and efficiency.