Multi-Task Learning for Compositional Data via Sparse Network Lasso

Multi-Task Learning for Compositional Data via Sparse Network Lasso

Multi-task learning is a statistical methodology that aims to improve the generalization performances of estimation and prediction tasks by sharing common information among multiple tasks. On the other hand, compositional data consist of proportions as components summing to one. Because components o...

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Bibliographic Details
Main Authors: Akira Okazaki, Shuichi Kawano
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Entropy
Subjects:
clustering
log-contrast model
multi-task learning
symmetric form
variable selection
Online Access:https://www.mdpi.com/1099-4300/24/12/1839

Internet

https://www.mdpi.com/1099-4300/24/12/1839

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