Semi-supervised Hierarchical Multimodal Feature and Sample Selection for Alzheimer's Disease Diagnosis.

Alzheimer's disease (AD) is a progressive neurodegenerative disease that impairs a patient's memory and other important mental functions. In this paper, we leverage the mutually informative and complementary features from both structural magnetic resonance imaging (MRI) and single nucleotide polymorphism (SNP) for improving the diagnosis. Due to the feature redundancy and sample outliers, direct use of all training data may lead to suboptimal performance in classification. In addition, as redundant features are involved, the most discriminative feature subset may not be identified in a single step, as commonly done in most existing feature selection approaches. Therefore, we formulate a hierarchical multimodal feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps. To positively guide the data manifold preservation, we utilize both labeled and unlabeled data in the learning process, making our method semi-supervised. The finally selected features and samples are then used to train support vector machine (SVM) based classification models. Our method is evaluated on 702 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and the superior classification results in AD related diagnosis demonstrate the effectiveness of our approach as compared to other methods.