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Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference

Adam, V; Hensman, J; Sahani, M; (2016) Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference. In: Proceedings of MLSP2016. IEEE Green open access

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Abstract

Many functions and signals of interest are formed by the addition of multiple underlying components, often nonlinearly transformed and modified by noise. Examples may be found in the literature on Generalized Additive Models [1] and Underdetermined Source Separation [2] or other mode decomposition techniques. Recovery of the underlying component processes often depends on finding and exploiting statistical regularities within them. Gaussian Processes (GPs) [3] have become the dominant way to model statistical expectations over functions. Recent advances make inference of the GP posterior efficient for large scale datasets and arbitrary likelihoods [4,5]. Here we extend these methods to the additive GP case [6, 7], thus achieving scalable marginal posterior inference over each latent function in settings such as those above.

Type: Proceedings paper
Title: Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference
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.2016.7738855
Publisher version: https://doi.org/10.1109/MLSP.2016.7738855
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
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/1544039
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