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ICML
2007
IEEE

Robust mixtures in the presence of measurement errors

10 years 6 days ago
Robust mixtures in the presence of measurement errors
We develop a mixture-based approach to robust density modeling and outlier detection for experimental multivariate data that includes measurement error information. Our model is designed to infer atypical measurements that are not due to errors, aiming to retrieve potentially interesting peculiar objects. Since exact inference is not possible in this model, we develop a tree-structured variational EM solution. This compares favorably against a fully factorial approximation scheme, approaching the accuracy of a Markov-Chain-EM, while maintaining computational simplicity. We demonstrate the benefits of including measurement errors in the model, in terms of improved outlier detection rates in varying measurement uncertainty conditions. We then use this approach for detecting peculiar quasars from an astrophysical survey, given photometric measurements with errors.
Ata Kabán, Jianyong Sun, Somak Raychaudhury
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2007
Where ICML
Authors Ata Kabán, Jianyong Sun, Somak Raychaudhury
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