Knowledge-Based Distant Regularization in Learning Probabilistic Models
Knowledge-Based Distant Regularization in Learning Probabilistic Models
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发表时间：
2018-06
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通讯作者：
Naoya Takeishi;Kosuke Akimoto
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作者：
Naoya Takeishi;Kosuke Akimoto
利用基于数据知识的适当归纳偏差对于实现统计机器学习的良好性能至关重要。然而，在实践中，感兴趣的领域知识往往只能远距离地提供数据属性之间关系的信息，这阻碍了在流行的正则化方法中直接利用这些领域知识。本文提出了基于知识的距离正则化框架，利用知识图中编码的距离信息对概率模型估计进行正则化。特别是，我们建议对知识图嵌入指定的模型参数施加先验分布。作为该框架的一个实例，我们提出了基于知识的远程正则化的因子分析模型。我们给出了改进该模型泛化能力的初步实验结果。
Exploiting the appropriate inductive bias based on the knowledge of data is essential for achieving good performance in statistical machine learning. In practice, however, the domain knowledge of interest often provides information on the relationship of data attributes only distantly, which hinders direct utilization of such domain knowledge in popular regularization methods. In this paper, we propose the knowledge-based distant regularization framework, in which we utilize the distant information encoded in a knowledge graph for regularization of probabilistic model estimation. In particular, we propose to impose prior distributions on model parameters specified by knowledge graph embeddings. As an instance of the proposed framework, we present the factor analysis model with the knowledge-based distant regularization. We show the results of preliminary experiments on the improvement of the generalization capability of such model.