Modelling Relational Data using Bayesian Clustered Tensor Factorization
Modelling Relational Data using Bayesian Clustered Tensor Factorization
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发表时间：
2009-12
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通讯作者：
I. Sutskever;R. Salakhutdinov;J. Tenenbaum
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作者：
I. Sutskever;R. Salakhutdinov;J. Tenenbaum
我们考虑学习概率模型的各种类型的对象之间的复杂关系结构的问题。一个模型至少可以通过两种方式帮助我们“理解”关系事实的数据集，一是通过在数据中找到可解释的结构，二是通过支持关于特定的未观察到的关系是否可能为真的预测或推断。这两个目标之间通常存在权衡：基于聚类的模型产生更容易解释的表示，而基于因子分解的方法在大型数据集上具有更好的预测性能。我们介绍了贝叶斯离散张量因子分解（BCTF）模型，它嵌入在一个非参数贝叶斯聚类框架的关系的因子分解表示。推理是完全贝叶斯的，但可以很好地扩展到大型数据集。该模型同时发现可解释的集群，并产生匹配或击败以前的关系数据概率模型的预测性能。
We consider the problem of learning probabilistic models for complex relational structures between various types of objects. A model can help us "understand" a dataset of relational facts in at least two ways, by finding interpretable structure in the data, and by supporting predictions, or inferences about whether particular unobserved relations are likely to be true. Often there is a tradeoff between these two aims: cluster-based models yield more easily interpretable representations, while factorization-based approaches have given better predictive performance on large data sets. We introduce the Bayesian Clustered Tensor Factorization (BCTF) model, which embeds a factorized representation of relations in a nonparametric Bayesian clustering framework. Inference is fully Bayesian but scales well to large data sets. The model simultaneously discovers interpretable clusters and yields predictive performance that matches or beats previous probabilistic models for relational data.