论文标题
学习基于矩阵分解的多相关数据聚类的间和内部术中和内部的模拟物
Learning Inter- and Intra-manifolds for Matrix Factorization-based Multi-Aspect Data Clustering
论文作者
论文摘要
由于其广泛的适用性，在具有多个方面的数据上聚类，例如多视图或多类关系数据。使用多种矩阵分解（NMF）框架使用流形学习的方法，该框架了解了多维数据的准确低级表示，已显示出有效性。我们建议在NMF框架中加入Manifold，利用不同数据类型（或视图）的数据点的距离信息来学习数据群集的多样性歧管。经验分析表明，所提出的方法可以找到各种相互关联类型的部分表示，并在聚类过程中选择有用的特征。在几个数据集上的结果表明，所提出的方法在准确性和效率方面都优于最先进的多相关数据聚类方法。
Clustering on the data with multiple aspects, such as multi-view or multi-type relational data, has become popular in recent years due to their wide applicability. The approach using manifold learning with the Non-negative Matrix Factorization (NMF) framework, that learns the accurate low-rank representation of the multi-dimensional data, has shown effectiveness. We propose to include the inter-manifold in the NMF framework, utilizing the distance information of data points of different data types (or views) to learn the diverse manifold for data clustering. Empirical analysis reveals that the proposed method can find partial representations of various interrelated types and select useful features during clustering. Results on several datasets demonstrate that the proposed method outperforms the state-of-the-art multi-aspect data clustering methods in both accuracy and efficiency.
