Nelson, JDB;
Nafornita, C;
Isar, A;
(2016)
Generalised M-Lasso for robust, spatially regularised hurst estimation.
In:
Proceedings of the 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP).
(pp. pp. 1265-1269).
IEEE: Orlando, FL, USA.
Preview |
Text
2015_globalsip.pdf - Accepted Version Download (386kB) | Preview |
Abstract
A generalised Lasso iteratively reweighted scheme is here introduced to perform spatially regularised Hurst estimation on semi-local, weakly self-similar processes. This is extended further to the robust, heavy-tailed case whereupon the generalised M-Lasso is proposed. The design successfully incorporates both a spatial derivative in the generalised Lasso regulariser operator and a weight matrix formulated in the wavelet domain. The result simultaneously spatially smooths the Hurst estimates and downweights outliers. Experiments using a Hampel score function confirm that the method yields superior Hurst estimates in the presence of strong outliers. Moreover, it is shown that the inferred weight matrix can be used to perform wavelet shrinkage and denoise fractional Brownian surfaces in the presence of strong, localised, band-limited noise.
| Type: | Proceedings paper |
|---|---|
| Title: | Generalised M-Lasso for robust, spatially regularised hurst estimation |
| Event: | 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) |
| ISBN-13: | 9781479975914 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/GlobalSIP.2015.7418401 |
| Publisher version: | http://dx.doi.org/10.1109/GlobalSIP.2015.7418401 |
| Language: | English |
| Additional information: | Copyright © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Keywords: | Conferences, Cost function, Estimation, Information processing, Noise reduction, Robustness, Signal resolution |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1477559 |
Archive Staff Only
 |
View Item |
