Multi-Task Learning for Compositional Data via Sparse Network Lasso

@article{Okazaki2021MultiTaskLF,
  title={Multi-Task Learning for Compositional Data via Sparse Network Lasso},
  author={Akira Okazaki and Shuichi Kawano},
  journal={Entropy},
  year={2021},
  volume={24}
}
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 of compositional data depend on each other, existing methods for multi-task learning cannot be directly applied to them. In the framework of multi-task learning, a network lasso regularization enables… 

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