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Communication Dans Un Congrès Année : 2013

Online change detection in exponential families with unknown parameters

Résumé

This paper studies online change detection in exponential families when both the parameters before and after change are unknown. We follow a standard statistical approach to sequential change detection with generalized likelihood ratio test statistics. We interpret these statistics within the framework of information geometry, hence providing a unified view of change detection for many common statistical models and corresponding distance functions. Using results from convex duality, we also derive an efficient scheme to compute the exact statistics sequentially, which allows their use in online settings where they are usually approximated for the sake of tractability. This is applied to real-world datasets of various natures, including onset detection in audio signals.
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Dates et versions

hal-00840662 , version 1 (02-07-2013)

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Arnaud Dessein, Arshia Cont. Online change detection in exponential families with unknown parameters. GSI 2013 First International Conference Geometric Science of Information, Aug 2013, Paris, France. pp.633-640, ⟨10.1007/978-3-642-40020-9_70⟩. ⟨hal-00840662⟩
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