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Error control for the detection of rare and weak signatures in massive data
| Content Provider | Hyper Articles en Ligne (HAL) |
|---|---|
| Author | Meillier, Céline Chatelain, Florent Michel, Olivier Ayasso, Hacheme |
| 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. |
| File Format | |
| Language | English |
| Publisher Date | 2015-09-01 |
| Access Restriction | Open |
| Subject Keyword | FDR error control massive data source detection matched filter info stat Computer Science [cs] Signal and Image Processing Statistics [stat] |
| Content Type | Text |
| Resource Type | Article |
