Multi-task learning for compositional data via sparse network lasso

HIGHLIGHTS

• who: Akira Okazaki and Shuichi Kawano from the Graduate School of Informatics and Engineering, The University of Electro-Communications, Chofugaoka, Chofu, Tokyo, Japan have published the research work: Multi-Task Learning for Compositional Data via Sparse Network Lasso, in the Journal: Entropy 2022, 1839 of December/13,/2022
• what: The authors propose a multi-task learning method for using a network lasso. The authors focus on a symmetric form of the log-contrast model which is a regression model with covariates. The authors report simulation studies conducted with the proposed method using artificial data.
 



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