Simultaneous clustering of multi-type relational data via symmetric nonnegative matrix tri-factorization
Simultaneous clustering of multi-type relational data via symmetric nonnegative matrix tri-factorization
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DOI：
10.1145/2063576.2063621
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发表时间：
2011-10
期刊：
影响因子：
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通讯作者：
Hua Wang;Heng Huang;C. Ding
中科院分区：
文献类型：
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作者：
Hua Wang;Heng Huang;C. Ding
互联网和现代技术的快速发展带来了涉及多种类型的对象的数据，这些对象彼此相关，称为多类型关系数据。传统的聚类方法对单一类型的数据很少能很好地工作，这就需要更先进的聚类技术来同时处理多种类型的数据，以利用它们之间的相关性。开发同时聚类方法的一个主要挑战是如何有效地使用包含在多类型关系数据集中的所有可用信息，包括类型间和类型内的关系。在本文中，我们提出了一个对称非负矩阵三因子分解（S-NMTF）框架，同时聚类多类型的关系数据。所提出的S-NMTF方法采用NMTF，同时聚类不同类型的数据使用它们的类型间的关系，并通过流形正则化纳入类型内的信息。为了解决S-NMTF中因子矩阵的对称使用问题，提出了一种新的一般矩阵不等式，推导了一种涉及四阶矩阵多项式的求解算法.实验结果验证了该方法的有效性。
The rapid growth of Internet and modern technologies has brought data involving objects of multiple types that are related to each other, called as multi-type relational data. Traditional clustering methods for single-type data rarely work well on them, which calls for more advanced clustering techniques to deal with multiple types of data simultaneously to utilize their interrelatedness. A major challenge in developing simultaneous clustering methods is how to effectively use all available information contained in a multi-type relational data set including inter-type and intra-type relationships. In this paper, we propose a Symmetric Nonnegative Matrix Tri-Factorization (S-NMTF) framework to cluster multi-type relational data at the same time. The proposed S-NMTF approach employs NMTF to simultaneously cluster different types of data using their inter-type relationships, and incorporate the intra-type information through manifold regularization. In order to deal with the symmetric usage of the factor matrix in S-NMTF, we present a new generic matrix inequality to derive the solution algorithm, which involves a fourth-order matrix polynomial, in a principled way. Promising experimental results have validated the proposed approach.