Clustering multivariate functional data with phase variation

Park, Juhyun and Ahn, Jeongyoun (2017) Clustering multivariate functional data with phase variation. Biometrics, 73 (1). pp. 324-333. ISSN 0006-341X

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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.

Item Type:
Journal Article
Journal or Publication Title:
Biometrics
Additional Information:
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.
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2700
Subjects:
ID Code:
80021
Deposited By:
Deposited On:
10 Jun 2016 15:36
Refereed?:
Yes
Published?:
Published
Last Modified:
20 Sep 2020 03:36