Home > Research > Publications & Outputs > Clustering multivariate functional data with ph...

Electronic data

  • ParkAhnBiometrics2016-earlyview

    Rights statement: This is the peer reviewed version of the following article: Park, J. and Ahn, J. (2016), Clustering multivariate functional data with phase variation. Biometrics. doi: 10.1111/biom.12546 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/biom.12546/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

    Accepted author manuscript, 2.84 MB, PDF document

    Available under license: CC BY: Creative Commons Attribution 4.0 International License

Links

Text available via DOI:

View graph of relations

Clustering multivariate functional data with phase variation

Research output: Contribution to journalJournal articlepeer-review

Published

Standard

Clustering multivariate functional data with phase variation. / Park, Juhyun; Ahn, Jeongyoun.

In: Biometrics, Vol. 73, No. 1, 03.2017, p. 324-333.

Research output: Contribution to journalJournal articlepeer-review

Harvard

APA

Vancouver

Author

Park, Juhyun ; Ahn, Jeongyoun. / Clustering multivariate functional data with phase variation. In: Biometrics. 2017 ; Vol. 73, No. 1. pp. 324-333.

Bibtex

@article{4eb0c5696f744c35838cc30e2cd214c6,
title = "Clustering multivariate functional data with phase variation",
abstract = "When functional data come as multiple curves per subject, characterizing the source of variations is not a trivial problem. The complexity of the problem goes deeper when there is phase variation in addition to amplitude variation. We consider clustering problem with multivariate functional data that have phase variations among the functional variables. We propose a conditional subject-specific warping framework in order to extract relevant features for clustering. Using multivariate growth curves of various parts of the body as a motivating example, we demonstrate the effectiveness of the proposed approach. The found clusters have individuals who show different relative growth patterns among different parts of the body.",
keywords = "Curve alignment, Functional clustering, Growth curves, Multivariate functional data, Phase variation",
author = "Juhyun Park and Jeongyoun Ahn",
note = "This is the peer reviewed version of the following article: Park, J. and Ahn, J. (2017), Clustering multivariate functional data with phase variation. Biom, 73: 324–333. doi:10.1111/biom.12546 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/biom.12546/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.",
year = "2017",
month = mar,
doi = "10.1111/biom.12546",
language = "English",
volume = "73",
pages = "324--333",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "1",

}

RIS

TY - JOUR

T1 - Clustering multivariate functional data with phase variation

AU - Park, Juhyun

AU - Ahn, Jeongyoun

N1 - This is the peer reviewed version of the following article: Park, J. and Ahn, J. (2017), Clustering multivariate functional data with phase variation. Biom, 73: 324–333. doi:10.1111/biom.12546 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/biom.12546/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

PY - 2017/3

Y1 - 2017/3

N2 - When functional data come as multiple curves per subject, characterizing the source of variations is not a trivial problem. The complexity of the problem goes deeper when there is phase variation in addition to amplitude variation. We consider clustering problem with multivariate functional data that have phase variations among the functional variables. We propose a conditional subject-specific warping framework in order to extract relevant features for clustering. Using multivariate growth curves of various parts of the body as a motivating example, we demonstrate the effectiveness of the proposed approach. The found clusters have individuals who show different relative growth patterns among different parts of the body.

AB - When functional data come as multiple curves per subject, characterizing the source of variations is not a trivial problem. The complexity of the problem goes deeper when there is phase variation in addition to amplitude variation. We consider clustering problem with multivariate functional data that have phase variations among the functional variables. We propose a conditional subject-specific warping framework in order to extract relevant features for clustering. Using multivariate growth curves of various parts of the body as a motivating example, we demonstrate the effectiveness of the proposed approach. The found clusters have individuals who show different relative growth patterns among different parts of the body.

KW - Curve alignment

KW - Functional clustering

KW - Growth curves

KW - Multivariate functional data

KW - Phase variation

U2 - 10.1111/biom.12546

DO - 10.1111/biom.12546

M3 - Journal article

VL - 73

SP - 324

EP - 333

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 1

ER -