Year: 2011
Type: article
Abstract: We introduce a Bayesian inference mechanism for outlier detection using the augmented Dirichlet process mixture. Outliers are detected by forming a maximum a posteriori (MAP) estimate of the data partition. Observations that comprise small or singleton clusters in the estimated partition are considered outliers. We offer a novel interpretation of the Dirichlet process precision parameter, and demonstrate its utility in outlier detection problems. The precision parameter is used to form an outlier detection criterion based on the Bayes factor for an outlier partition versus a class of partitions with fewer or no outliers. We further introduce a computational method for MAP estimation that is free of posterior sampling, and guaranteed to find a MAP estimate in finite time. The novel methods are compared with several established strategies in a yeast microarray time series.
Source: Bayesian Analysis
DOI: 10.1214/11-ba625
Authors: Matthew S. Shotwell, Elizabeth H. Slate
Institutions: Vanderbilt University, Medical University of South Carolina
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W1553350739