HMTL - Heterogeneous Multi-Task Feature Learning
The heterogeneous multi-task feature learning is a data
integration method to conduct joint feature selection across
multiple related data sets with different distributions. The
algorithm can combine different types of learning tasks,
including linear regression, Huber regression, adaptive Huber,
and logistic regression. The modified version of Bayesian
Information Criterion (BIC) is produced to measure the model
performance. Package is based on Yuan Zhong, Wei Xu, and Xin
Gao (2022)
<https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.