Generalized fused group lasso regularized multi-task feature learning for predicting cognitive outcomes in Alzheimers disease
Objective
Alzheimers disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regression analysis has been widely applied to AD research to relate clinical and biomarker data such as predicting cognitive outcomes from Magnetic Resonance Imaging (MRI) measures. Recently, the multi-task feature learning (MTFL) methods have been widely studied to predict cognitive outcomes and select the discriminative feature subset from MRI features by incorporating inherent correlations among multiple clinical cognitive measures. However, the existing MTFL assumes the correlation among all the tasks is uniform, and the task relatedness is modeled by encouraging a common subset of features with neglecting the inherent structure of tasks and MRI features.
Methods
In this paper, we proposed a generalized fused group lasso (GFGL) regularization to model the underlying structures, involving (1) a graph structure within tasks and (2) a group structure among the image features. Then, we present a multi-task learning framework (called GFGL–MTFL), combining the ℓ2, 1-norm with the GFGL regularization, to model the flexible structures.
Results
Through empirical evaluation and comparison with different baseline methods and the state-of-the-art MTL methods on data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, we illustrate that the proposed GFGL–MTFL method outperforms other methods in terms of both Mean Squared Error (nMSE) and weighted correlation coefficient (wR). Improvements are statistically significant for most scores (tasks).
Conclusions
The experimental results with real and synthetic data demonstrate that incorporating the two prior structures by the generalized fused group lasso norm into the multi task feature learning can improve the prediction performance over several state-of-the-art competing methods, and the estimated correlation of the cognitive functions and the identification of cognition relevant imaging markers are clinically and biologically meaningful.
Citation
P. Cao,
X. Liu,
J. Yang,
D. Zhao,
M. Huang,
O. Zaiane.
"Generalized fused group lasso regularized multi-task feature learning for predicting cognitive outcomes in Alzheimers disease". Computer Methods and Programs in Biomedicine, 162, pp 19-45, August 2018.
| Keywords: |
Alzheimer’s disease, Regression, Multi-task learning, Sparse learning, Group lasso, Fused lasso, Biomarker identification |
| Category: |
In Journal |
| Web Links: |
ScienceDirect |
BibTeX
@article{Cao+al:18,
author = {Peng Cao and Xiaoli Liu and Jinzhu Yang and Dazhe Zhao and Min
Huang and Osmar R. Zaiane},
title = {Generalized fused group lasso regularized multi-task feature
learning for predicting cognitive outcomes in Alzheimers disease},
Volume = "162",
Pages = {19-45},
journal = {Computer Methods and Programs in Biomedicine},
year = 2018,
}Last Updated: October 29, 2019
Submitted by Sabina P 
