The nested indian buffet process for flexible topic modeling
The nested indian buffet process for flexible topic modeling
复制标题
嵌套的印度自助餐流程，实现灵活的主题建模
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
Ying
中科院分区：
文献类型：
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作者：
Jen;Ying
提出了一种基于嵌套印度自助餐过程的fl可扩展主题模型。fl的灵活性是通过重新释放三个约束来实现的：(1)主题的数量是fi固定的，(2)主题是独立的，(3)文档的主题层次受单个树路径的限制。贝叶斯非参数学习被用来建立一个树模型，其中主题的数量和主题层次结构是从给定的数据中自动学习的。特别是，我们提出了NIBP来构建用于表示异质文档的主题混合模型，其中混合组件是fl从文档或客户在印度自助餐过程中选择的树节点或菜肴中选择的。该选择以嵌套和分层的方式执行。在文档表示上的实验表明，该方法具有良好的fi性能。
This paper presents a flexible topic model based on the nested Indian buffet process (nIBP). The flexibility is achieved by re-laxing three constraints: (1) number of topics is fixed, (2) topics are independent, and (3) topic hierarchy for a document is limited by a single tree path. Bayesian nonparametric learning is conducted to build a tree model where the number of topics and the topic hierarchies are automatically learnt from the given data. In particular, we propose the nIBP to construct the topic mixture model for representation of heterogeneous documents where the mixture components are flexibly selected from tree nodes or dishes that a document or customer chooses in Indian buffet process. The selection is performed in a nested and hierarchical manner. The experiments on document representation show the benefits of using the proposed nIBP.