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 in detecting peculiar quasars from an astrophysical survey,
given photometric measurements with errors.Comment: (Refereed) Proceedings of the 24-th Annual International Conference
on Machine Learning 2007 (ICML07), (Ed.) Z. Ghahramani. June 20-24, 2007,
Oregon State University, Corvallis, OR, USA, pp. 847-854; Omnipress. ISBN
978-1-59593-793-3; 8 pages, 6 figure
