A fast algorithm for robust mixtures in the presence of measurement errors

IEEE Transactions on Neural Networks
Jianyong Sun, Ata Kaban

Abstract

In experimental and observational sciences, detecting atypical, peculiar data from large sets of measurements has the potential of highlighting candidates of interesting new types of objects that deserve more detailed domain-specific followup study. However, measurement data is nearly never free of measurement errors. These errors can generate false outliers that are not truly interesting. Although many approaches exist for finding outliers, they have no means to tell to what extent the peculiarity is not simply due to measurement errors. To address this issue, we have developed a model-based approach to infer genuine outliers from multivariate data sets when measurement error information is available. This is based on a probabilistic mixture of hierarchical density models, in which parameter estimation is made feasible by a tree-structured variational expectation-maximization algorithm. Here, we further develop an algorithmic enhancement to address the scalability of this approach, in order to make it applicable to large data sets, via a K-dimensional-tree based partitioning of the variational posterior assignments. This creates a non-trivial tradeoff between a more detailed noise model to enhance the detection accuracy, and t...Continue Reading

References

Oct 6, 2004·IEEE Transactions on Neural Networks·Antti Honkela, Harri Valpola
Nov 25, 2005·Computers in Biology and Medicine·Alfredo Vellido, Paulo J G Lisboa
Oct 3, 2006·Neural Networks : the Official Journal of the International Neural Network Society·Cédric Archambeau, Michel Verleysen
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Mar 13, 2009·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Dimitris G TzikasNikolaos P Galatsanos

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Citations

May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·Jianyong Sun, Simeon Keates
Oct 13, 2011·IEEE Transactions on Neural Networks·David HeMikhail Zade
Apr 1, 2017·IEEE Transactions on Neural Networks and Learning Systems·Jianyong SunShengbin Liao
Mar 11, 2018·Scientific Reports·Peiyang LiPeng Xu

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