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Conference Papers Year : 2015

Error control for the detection of rare and weak signatures in massive data

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Abstract

In this paper, we address the general issue of detecting rare and weak signatures in very noisy data. Multiple hypotheses testing approaches can be used to extract a list of components of the data that are likely to be contaminated by a source while controlling a global error criterion. However most of efficients methods available in the literature are derived for independent tests. Based on the work of Benjamini and Yekutieli [1], we show that under some classical positiv-ity assumptions, the Benjamini-Hochberg procedure for False Discovery Rate (FDR) control can be directly applied to the result produced by a very common tool in signal and image processing: the matched filter. This shows that despite the dependency structure between the components of the matched filter output, the Benjamini-Hochberg procedure still guarantee the FDR control. This is illustrated on both synthetic and real data.
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Dates and versions

hal-01198717 , version 1 (14-09-2015)

Identifiers

  • HAL Id : hal-01198717 , version 1

Cite

Céline Meillier, Florent Chatelain, Olivier Michel, H Ayasso. Error control for the detection of rare and weak signatures in massive data. EUSIPCO 2015 - 23th European Signal Processing Conference, Aug 2015, Nice, France. ⟨hal-01198717⟩
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