Structured sparsity regularized multiple kernel learning for Alzheimer’s disease diagnosis
作者：
Highlights：
• For Alzheimer’s disease (AD) diagnosis, we consider the integration of multi-modality imaging and genetic data which encode different level of knowledge.
• With a focus on taking advantage of both phenotype and genotype information, a novel structured sparsity, defined by ℓ1, p -norm (p > 1), regularized multiple kernel learning method is designed.
• An efficient block coordinate descent algorithm applicable to any case with p > 1 was derived to solve the proposed formulation.
摘要
•For Alzheimer’s disease (AD) diagnosis, we consider the integration of multi-modality imaging and genetic data which encode different level of knowledge.•With a focus on taking advantage of both phenotype and genotype information, a novel structured sparsity, defined by ℓ1, p -norm (p > 1), regularized multiple kernel learning method is designed.•An efficient block coordinate descent algorithm applicable to any case with p > 1 was derived to solve the proposed formulation.
论文关键词：Structured sparsity,Multimodal features,Multiple kernel learning,Feature selection,Alzheimer’s disease diagnosis
论文评审过程：Received 29 April 2018, Revised 3 November 2018, Accepted 23 November 2018, Available online 24 November 2018, Version of Record 3 December 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.11.027
