A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments
A Nonparametric Bayesian Method for Inferring Features From Similarity Judgments
复制标题
一种从相似性判断推断特征的非参数贝叶斯方法
DOI：
10.7551/mitpress/7503.003.0134
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发表时间：
2006
影响因子：
2.2
通讯作者：
T. Griffiths
中科院分区：
文献类型：
--
作者：
D. Navarro;T. Griffiths
加性聚类模型被广泛用于从一组刺激的相似性推断它们的特征，假设相似性是共同特征的加权线性函数。本文开发了一个完整的贝叶斯公式的添加剂聚类模型，使用非参数贝叶斯统计的方法，允许不同的功能的数量。我们用它来探索参数估计的几种方法，表明非参数贝叶斯方法提供了一种简单的方法来获得用于产生相似性判断的特征数量及其重要性的估计。
The additive clustering model is widely used to infer the features of a set of stimuli from their similarities, on the assumption that similarity is a weighted linear function of common features. This paper develops a fully Bayesian formulation of the additive clustering model, using methods from nonparametric Bayesian statistics to allow the number of features to vary. We use this to explore several approaches to parameter estimation, showing that the nonparametric Bayesian approach provides a straightforward way to obtain estimates of both the number of features used in producing similarity judgments and their importance.