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Convolutional higher order matching pursuit

Bohner, G; Sahani, M; (2016) Convolutional higher order matching pursuit. In: Proceedings of MLSP2016. IEEE Green open access

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Abstract

We introduce a greedy generalised convolutional algorithm to efficiently locate an unknown number of sources in a series of (possibly multidimensional) images, where each source contributes a localised and low-dimensional but otherwise variable signal to its immediate spatial neighbourhood. Our approach extends convolutional matching pursuit in two ways: first, it takes the signal generated by each source to be a variable linear combination of aligned dictionary elements; and second, it executes the pursuit in the domain of high-order multivariate cumulant statistics. The resulting algorithm adapts to varying signal and noise distributions to flexibly recover source signals in a variety of settings.

Type: Proceedings paper
Title: Convolutional higher order matching pursuit
Event: 26th IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
Location: Salerno, ITALY
Dates: 13 September 2016 - 16 September 2016
ISBN-13: 978-1-5090-0747-9
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/MLSP.2016.7738847
Publisher version: https://doi.org/10.1109/MLSP.2016.7738847
Language: English
Additional information: © 2016 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: Science & Technology, Technology, Engineering, Electrical & Electronic, Engineering, Matching pursuit, feature decomposition, higher order, multi-sample, convolutional
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
URI: http://discovery.ucl.ac.uk/id/eprint/1543255
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