Manifold regularized multitask feature learning for multimodality disease classification.

ABSTRACT: Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint selection of common features across multiple modalities. However, one disadvantage of existing multimodality based methods is that they ignore the useful data distribution information in each modality, which is essential for subsequent classification. Accordingly, in this paper we propose a manifold regularized multitask feature learning method to preserve both the intrinsic relatedness among multiple modalities of data and the data distribution information in each modality. Specifically, we denote the feature learning on each modality as a single ta

SUBMITTER: Jie B 

PROVIDER: S-EPMC4470367 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature