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Activity Number: 335 - SPEED: Reliable Statistical Learning and Data Science
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #323275 View Presentation
Title: Variable Selection on Functional Data Using Kernel Machine
Author(s): Haoyu Wang*
Companies: North Carolina State University
Keywords: Functional Data ; Machine Learning ; Variable Selection
Abstract:

In the modern research world, data with functional predictors are increasingly common. We propose a new variable selection technique, when the predictors are functional and the response is scalar. The method is based on a very flexible nonparametric model and we use functional PCA, Gaussian process and similarity regression with L1 penalty to select variables. The method is characterized by high interpretability and computational efficiency. The method is used on hand movement dataset to help enhance health and mobility of lower limb amputees.


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

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