文章基本信息

标题：Simultaneous Higher-order Relation Prediction via Collective Incidence Matrix Embedding
本地全文：下载
作者：Nozomi Nori ; Danushka Bollegala ; Hisashi Kashima 等
期刊名称：人工知能学会論文誌
印刷版ISSN：1346-0714
电子版ISSN：1346-8030
出版年度：2015
卷号：30
期号：2
页码：459-465
DOI：10.1527/tjsai.30.459
出版社：The Japanese Society for Artificial Intelligence
摘要：We propose a prediction method for higher-order relational data from multiple sources. The high-dimensional property of higher-order relations causes problems associated with sparse observations. To cope with this problem, we propose a method to integrate higher-order relational data from multiple sources. Our target task is the simultaneous decomposition of higher-order, multi-relational data, which corresponds to the simultaneous decomposition of multiple tensors. However, we transform each tensor into an incidence matrix for the corresponding hypergraph and apply a nonlinear dimensionality reduction technique that results in a generalized eigenvalue problem guaranteeing global optimal solutions. We also extend our method to incorporate objects' attribute information to improve prediction for unseen/unobserved objects. To the best of our knowledge, this is the first reported method that can make predictions for (1) higher-order relations (2) with multi-relational data (3) with object attribute information and which (4) guarantees global optimal solutions. Using real-world datasets from social web services, we demonstrate that our proposed method is more robust against data sparsity than state-of-the-art methods for higher-order, single/multi-relational data including nonnegative multiple tensor factorization.
关键词：higher-order relational data ; multi-relational data ; incidence matrix ; nonlinear dimensionality reduction