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

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 expectationmaximization algorithm. Here, we further develop an algorithmic enhancement to address the scalability of this app...

• Type: Journal
• Year: 2010
• Where: TNN
• Authors: Jianyong Sun, Ata Kabán
• Keywords: Management, Measurement, Measurement Error, Measurement Error Information, TNN 2010