Meta-Path based Nonnegative Matrix Factorization for clustering on multi-type relational data
Zhao,Yangyang; Sun,Zhengya; Xu,Changsheng; Hao,Hongwei
2015-07
会议名称The 2015 International Joint Conference on Neural Networks (IJCNN 2015)
会议录名称Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN 2015)
会议日期July 12-17, 2015
会议地点Killarney, Ireland
摘要
Clustering on multi-type relational data has attracted increasing interest due to its great practical and theoretical importance. One of the most popular solutions is nonnegative
matrix factorization. However, previous work on non negative matrix factorization typically copes with multi-type relations individually, and ignores the underlying semantics conveyed by the relation propagation. Additionally, these approaches may suffer from data sparsity as most of the relations between object pairs are unknown. In this paper we propose a novel Meta-Path based Nonnegative Matrix Factorization (MPNMF) framework,
which enriches potentially useful similarity semantics for the improved clustering performance. We begin with constructing meta-paths, i.e., paths that connects object types via a sequence of relations, which are appropriately weighted according to certain propagation decay rules. Based on the weighted meta-paths, we are promised to characterize the strength of pairwise interactions among the objects. Together with the attributes in the bag-of-word form, we cluster the objects of target type by collective
nonnegative matrix factorization. Experiments on real world datasets demonstrate the effectiveness of our method.
关键词Multi-type Relational Data Clustering Collective Nonnegative Matrix Factorization
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11952
专题模式识别国家重点实验室_多媒体计算与图形学
通讯作者Sun,Zhengya
作者单位Institute of Automation, Chinese Academy of Sciences
推荐引用方式
GB/T 7714
Zhao,Yangyang,Sun,Zhengya,Xu,Changsheng,et al. Meta-Path based Nonnegative Matrix Factorization for clustering on multi-type relational data[C],2015.
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