Segment parameter labelling in MCMC mean-shift change detection
File(s)
Accepted version
Author(s)
Ahrabian, Alireza
Enshaeifar, Shirin
Cheong-Took, Clive
Barnaghi, Payam
Type
Conference Paper
Abstract
This work addresses the problem of segmentation in time series data with respect to a statistical parameter of interest in Bayesian models. It is common to assume that the parameters are distinct within each segment. As such, many Bayesian change point detection models do not exploit the segment parameter patterns, which can improve performance. This work proposes a Bayesian mean-shift change point detection algorithm that makes use of repetition in segment parameters, by introducing segment class labels that utilise a Dirichlet process prior. The performance of the proposed approach was assessed on both synthetic and real world data, highlighting the enhanced performance when using parameter labelling.
Date Issued
2018
Date Acceptance
2018-04-15
Citation
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp.4244-4248
ISBN
978-1-5386-4658-8
ISSN
2379-190X
Publisher
IEEE
Start Page
4244
End Page
4248
Journal / Book Title
2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Copyright Statement
Copyright © 2018 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.
Identifier
https://ieeexplore.ieee.org/document/8462438
Source
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Subjects
Acoustics
Dirichlet Process
Engineering
Engineering, Electrical & Electronic
Markov Chain Monte Carlo
Mean-Shift Change Detection
MODEL
Nonparametric Bayesian
Science & Technology
SIGNAL SEGMENTATION
Technology
Publication Status
Published
Start Date
2018-04-15
Finish Date
2018-04-20
Coverage Spatial
Calgary, AB, Canada
Date Publish Online
2018-09-13