TY - GEN
T1 - A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression
AU - Boschi, Tobia
AU - Reimherr, Matthew
AU - Chiaromonte, Francesca
N1 - Funding Information:
We thank Kateryna Makova and her laboratory at Penn State for access to the INSIGHT data, Ana Kenney for help with the data and useful discussions. The work of Tobia Boschi and Francesca Chiaromonte was partially supported by the Huck Institutes of the Life Sciences at Penn State, the work of Matthew Reimherr was partially supported by the Grant NSF SES-1853209.
Publisher Copyright:
© 2021 Neural information processing systems foundation. All rights reserved.
PY - 2021
Y1 - 2021
N2 - Feature Selection and Functional Data Analysis are two dynamic areas of research, with important applications in the analysis of large and complex data sets. Straddling these two areas, we propose a new highly efficient algorithm to perform Group Elastic Net with application to function-on-scalar feature selection, where a functional response is modeled against a very large number of potential scalar predictors. First, we introduce a new algorithm to solve Group Elastic Net in ultrahigh dimensional settings, which exploits the sparsity structure of the Augmented Lagrangian to greatly reduce computational burden. Next, taking advantage of the properties of Functional Principal Components, we extend our algorithm to the function-on-scalar regression framework. We use simulations to demonstrate the CPU time gains afforded by our approach compared to its best existing competitors, and present an application to data from a Genome Wide Association Study on childhood obesity.
AB - Feature Selection and Functional Data Analysis are two dynamic areas of research, with important applications in the analysis of large and complex data sets. Straddling these two areas, we propose a new highly efficient algorithm to perform Group Elastic Net with application to function-on-scalar feature selection, where a functional response is modeled against a very large number of potential scalar predictors. First, we introduce a new algorithm to solve Group Elastic Net in ultrahigh dimensional settings, which exploits the sparsity structure of the Augmented Lagrangian to greatly reduce computational burden. Next, taking advantage of the properties of Functional Principal Components, we extend our algorithm to the function-on-scalar regression framework. We use simulations to demonstrate the CPU time gains afforded by our approach compared to its best existing competitors, and present an application to data from a Genome Wide Association Study on childhood obesity.
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M3 - Conference contribution
AN - SCOPUS:85131839907
T3 - Advances in Neural Information Processing Systems
SP - 9264
EP - 9277
BT - Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021
A2 - Ranzato, Marc'Aurelio
A2 - Beygelzimer, Alina
A2 - Dauphin, Yann
A2 - Liang, Percy S.
A2 - Wortman Vaughan, Jenn
PB - Neural information processing systems foundation
T2 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021
Y2 - 6 December 2021 through 14 December 2021
ER -
