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Activity Number: 79 - Functional Data Analysis: Methods and Applications
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #304481
Title: Multivariate Functional Data Clustering with Variable Selection and an Application to Sensory Data
Author(s): Zhongnan Jin* and Yili Hong
Companies: Virginia Tech and Virginia Tech
Keywords: sensory data; multivariate functional principal component analysis; E-M algorithm; group penalty; mixture Gaussian model; group lasso
Abstract:

In this paper, we conduct multivariate functional data clustering in an unsupervised manner. Dimensions of high volume data are significantly reduced by using only partial multivariate functional principal components. The multivariate functional principal component analysis (MFPCA) enables us to transform multi-dimensional and continuously measured data into an orthonormal matrix, where each dimension in the original data can be expressed by corresponding columns in the transformed matrix. In this way, clustering techniques can be applied to the transformed matrix. In our study, we assume this transformed data follow a Gaussian mixture model with K distinct centers, and the covariance matrix is the same across all clusters. We use penalty based maximum likelihood to conduct clustering with automatic variable selection. In addition, grouped variable selection is considered in this study. Because principal scores belonging to the same original function should have the same tendency to be in or out for the variable selection procedure. An application of engineer system sensory data is studied. Model performances are evaluated in the simulation study.


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

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