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Activity Number: 406 - Novel Approaches for Handling Complex Data in Treatment Diagnosis and Evaluation
Type: Topic-Contributed
Date/Time: Thursday, August 12, 2021 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #317415
Title: Optimal Linear Transformations of Functional Data for Clustering Methods
Author(s): Hanchao Zhang*
Companies: New York University Grossman School of Medicine
Keywords: Clustering; Linear Transformation; Semi-Supervised Clustering

Most clustering methods are completely unsupervised and perform poorly in high-dimensional settings. In this talk, we consider a semi-supervised clustering approach that incorporates covariate information. In order to optimize the clustering, we consider pre-conditioning using linear transformations (such as projections and stretching) to align and direct clustering algorithms with known covariate subgroupings. The optimization criterion for the clustering is based on the variation of information (Meilia 2007). This extension on identifying possible subgroups based on current labels has multiple applications in medical research fields that collect functional data. For example, related diagnostic groups may share common functional trajectories and some trajectories may be unique to a particular diagnostic group. Our approach is motivated to optimize clustering in these settings.

Authors who are presenting talks have a * after their name.

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