CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算与图形学
Meta-Path based Nonnegative Matrix Factorization for clustering on multi-type relational data
Zhao,Yangyang; Sun,Zhengya; Xu,Changsheng; Hao,Hongwei
Conference NameThe 2015 International Joint Conference on Neural Networks (IJCNN 2015)
Source PublicationProceedings of the 2015 International Joint Conference on Neural Networks (IJCNN 2015)
Conference DateJuly 12-17, 2015
Conference PlaceKillarney, Ireland
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.
KeywordMulti-type Relational Data Clustering Collective Nonnegative Matrix Factorization
Indexed ByEI
Document Type会议论文
Corresponding AuthorSun,Zhengya
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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