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    Rights statement: This is the author’s version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Signal Processing, 152, 2018 DOI: 10.1016/j.sigpro.2018.01.005

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Dynamic Classification using Multivariate Locally Stationary Wavelet Processes

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Dynamic Classification using Multivariate Locally Stationary Wavelet Processes. / Park, Timothy; Eckley, Idris A.; Ombao, Hernando C.

In: Signal Processing, Vol. 152, 11.2018, p. 118-129.

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Park, Timothy ; Eckley, Idris A. ; Ombao, Hernando C. / Dynamic Classification using Multivariate Locally Stationary Wavelet Processes. In: Signal Processing. 2018 ; Vol. 152. pp. 118-129.

Bibtex

@article{cd9fcaf7355a48f797389788287c0151,
title = "Dynamic Classification using Multivariate Locally Stationary Wavelet Processes",
abstract = "Methods for the supervised classification of signals generally aim to assign a signal to one class for its entire time span. In this paper we present an alternative formulation for multivariate signals where the class membership is permitted to change over time. Our aim therefore changes from classifying the signal as a whole to classifying the signal at each time point to one of a fixed number of known classes. We assume that each class is characterised by a different stationary generating process, the signal as a whole will however be nonstationary due to class switching. To capture this nonstationarity we use the recently proposed Multivariate Locally Stationary Wavelet model. To account for uncertainty in class membership at each time point our goal is not to assign a definite class membership but rather to calculate the probability of a signal belonging to a particular class. Under this framework we prove some asymptotic consistency results. This method is also shown to perform well when applied to both simulated and accelerometer data. In both cases our method is able to place a high probability on the correct class for the majority of time points.",
keywords = "Wavelets, Local stationarity, Multivariate signals, Coherence, Partial coherence,",
author = "Timothy Park and Eckley, {Idris A.} and Ombao, {Hernando C.}",
note = "This is the author’s version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Signal Processing, 152, 2018 DOI: 10.1016/j.sigpro.2018.01.005",
year = "2018",
month = "11",
doi = "10.1016/j.sigpro.2018.01.005",
language = "English",
volume = "152",
pages = "118--129",
journal = "Signal Processing",
issn = "0165-1684",
publisher = "Elsevier",

}

RIS

TY - JOUR

T1 - Dynamic Classification using Multivariate Locally Stationary Wavelet Processes

AU - Park, Timothy

AU - Eckley, Idris A.

AU - Ombao, Hernando C.

N1 - This is the author’s version of a work that was accepted for publication in Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Signal Processing, 152, 2018 DOI: 10.1016/j.sigpro.2018.01.005

PY - 2018/11

Y1 - 2018/11

N2 - Methods for the supervised classification of signals generally aim to assign a signal to one class for its entire time span. In this paper we present an alternative formulation for multivariate signals where the class membership is permitted to change over time. Our aim therefore changes from classifying the signal as a whole to classifying the signal at each time point to one of a fixed number of known classes. We assume that each class is characterised by a different stationary generating process, the signal as a whole will however be nonstationary due to class switching. To capture this nonstationarity we use the recently proposed Multivariate Locally Stationary Wavelet model. To account for uncertainty in class membership at each time point our goal is not to assign a definite class membership but rather to calculate the probability of a signal belonging to a particular class. Under this framework we prove some asymptotic consistency results. This method is also shown to perform well when applied to both simulated and accelerometer data. In both cases our method is able to place a high probability on the correct class for the majority of time points.

AB - Methods for the supervised classification of signals generally aim to assign a signal to one class for its entire time span. In this paper we present an alternative formulation for multivariate signals where the class membership is permitted to change over time. Our aim therefore changes from classifying the signal as a whole to classifying the signal at each time point to one of a fixed number of known classes. We assume that each class is characterised by a different stationary generating process, the signal as a whole will however be nonstationary due to class switching. To capture this nonstationarity we use the recently proposed Multivariate Locally Stationary Wavelet model. To account for uncertainty in class membership at each time point our goal is not to assign a definite class membership but rather to calculate the probability of a signal belonging to a particular class. Under this framework we prove some asymptotic consistency results. This method is also shown to perform well when applied to both simulated and accelerometer data. In both cases our method is able to place a high probability on the correct class for the majority of time points.

KW - Wavelets

KW - Local stationarity

KW - Multivariate signals

KW - Coherence

KW - Partial coherence,

U2 - 10.1016/j.sigpro.2018.01.005

DO - 10.1016/j.sigpro.2018.01.005

M3 - Journal article

VL - 152

SP - 118

EP - 129

JO - Signal Processing

JF - Signal Processing

SN - 0165-1684

ER -