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.
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -