Group Guided Sparse Group Lasso Multi-task Learning (GSGL-MTL)

This repository contains a MATLAB implementation of the GSGL-MTL algorithm proposed in the paper Group Guided Sparse Group Lasso Multi-task Learning for Cognitive Performance Prediction of Alzheimer’s Disease.

Overview

The Group guided Sparse Group Lasso regularized multi-task learning (GSGL-MTL) algorithm exploits both the group structure of features and the multi-task correlation, to unify feature-level and ROI-level analysis in an unified multi-task learning framework. An Alternating Direction Method of Multipliers (ADMM) based optimization is developed to effciently solve the non-smooth formulation.

This code has been tested only in MATLAB in both Linux and Mac.

How to run?

We created the file GSGL_MTL.m to show how to run GSGL-MTL code.

Structure of the input data files

In order to run the code the input data files containing the training and test data must follow a specific format. The ADMM() function, which is the core algorithm of GSGL-MTL, receives two matrices, X (covariate matrix) n x p with the number of n samples and p covariates, and Y (response matrix) n x k with k tasks; and group information vector with p covariates divided q disjoint groups. Note that, the number of features in each group can be different.

How to cite it?

If you like it and want to cite it in your papers, you can use the following:

#!latex

@inproceedings{liu2017group,
  title={Group Guided Sparse Group Lasso Multi-task Learning for Cognitive Performance Prediction of Alzheimer’s Disease},
  author={Liu, Xiaoli and Cao, Peng and Zhao, Dazhe and Zaiane, Osmar and others},
  booktitle={International Conference on Brain Informatics},
  pages={202--212},
  year={2017},
  organization={Springer}
}

Have a question?

If you found any bug or have a question, don't hesitate to contact me:

[Xiaoli Liu] email: neuxiaoliliu -at- gmail -dot- com