Sparse shared structure based multi-task learning for MRI based cognitive performance prediction of Alzheimer’s disease
作者：
Highlights：
• A mixed sparse shared structure based multi-task learning is proposed.
• The formulation can be applied on regression, classification or clustering.
• An efficient optimization algorithm is derived to solve the nonsmooth formulation.
• Experimental results demonstrate significant performance improvements over the existing methods.
• Our method is able to discover the biomarkers relevant to cognitive performance and fuse the multi-modality data.
摘要
•A mixed sparse shared structure based multi-task learning is proposed.•The formulation can be applied on regression, classification or clustering.•An efficient optimization algorithm is derived to solve the nonsmooth formulation.•Experimental results demonstrate significant performance improvements over the existing methods.•Our method is able to discover the biomarkers relevant to cognitive performance and fuse the multi-modality data.
论文关键词：Alzheimer’s disease,Multi-task learning,Proximal gradient,Regression,Biomarker discovery
论文评审过程：Received 4 November 2016, Revised 7 June 2017, Accepted 17 July 2017, Available online 18 July 2017, Version of Record 28 July 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.07.018
