Structured sparsity regularized multiple kernel learning for Alzheimer's disease diagnosis.

ABSTRACT: Multimodal data fusion has shown great advantages in uncovering information that could be overlooked by using single modality. In this paper, we consider the integration of high-dimensional multi-modality imaging and genetic data for Alzheimer's disease (AD) diagnosis. With a focus on taking advantage of both phenotype and genotype information, a novel structured sparsity, defined by ℓ _{1, p}-norm (p > 1), regularized multiple kernel learning method is designed. Specifically, to facilitate structured feature selection and fusion from heterogeneous modalities and also capture feature-wise importance, we represent each feature with a distinct kernel as a basis, followed by grouping the kernels according to modalities. Then, an optimally combined kernel pr

SUBMITTER: Peng J 

PROVIDER: S-EPMC6410562 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature