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May 2019 The coreset variational Bayes (CVB) algorithm for mixture analysis
Qianying Liu, Clare A. McGrory, Peter W. J. Baxter
Braz. J. Probab. Stat. 33(2): 267-279 (May 2019). DOI: 10.1214/17-BJPS387

Abstract

The pressing need for improved methods for analysing and coping with big data has opened up a new area of research for statisticians. Image analysis is an area where there is typically a very large number of data points to be processed per image, and often multiple images are captured over time. These issues make it challenging to design methodology that is reliable and yet still efficient enough to be of practical use. One promising emerging approach for this problem is to reduce the amount of data that actually has to be processed by extracting what we call coresets from the full dataset; analysis is then based on the coreset rather than the whole dataset. Coresets are representative subsamples of data that are carefully selected via an adaptive sampling approach. We propose a new approach called coreset variational Bayes (CVB) for mixture modelling; this is an algorithm which can perform a variational Bayes analysis of a dataset based on just an extracted coreset of the data. We apply our algorithm to weed image analysis.

Citation

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Qianying Liu. Clare A. McGrory. Peter W. J. Baxter. "The coreset variational Bayes (CVB) algorithm for mixture analysis." Braz. J. Probab. Stat. 33 (2) 267 - 279, May 2019. https://doi.org/10.1214/17-BJPS387

Information

Received: 1 February 2017; Accepted: 1 November 2017; Published: May 2019
First available in Project Euclid: 4 March 2019

zbMATH: 07057447
MathSciNet: MR3919023
Digital Object Identifier: 10.1214/17-BJPS387

Rights: Copyright © 2019 Brazilian Statistical Association

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Vol.33 • No. 2 • May 2019
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