Dirichlet process mixture models for finding shared structure between two related data sets

被引：0

作者：

Leen, Gayle ^{[1
]}

Fyfe, Colin ^{[1
]}

机构：

[1] Univ Paisley, Sch Comp, Paisley PA1 2BE, Renfrew, Scotland

来源：

ADVANCES ON ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING AND DATA BASES, PROCEEDINGS | 2008年

关键词：

nonparametric Bayesian methods; Dirichlet processes; probabilistic canonical correlation analysis; mixture models;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A nonparametric Bayesian approach is used for the problem of learning from two related data sets. We model the shared structure between two data sets using a Dirichlet process mixture model of probabilistic canonical correlation analysers, which allows the flexibility of the mappings from shared feature to data spaces to be automatically determined from the data.

引用

页码：31 / +

页数：3

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