Bi-level multi-source learning for heterogeneous block-wise missing data

• Published in: NeuroImage (Orlando, Fla.) Vol. 102; pp. 192 - 206
• Main Authors: Xiang, Shuo, Yuan, Lei, Fan, Wei, Wang, Yalin, Thompson, Paul M., Ye, Jieping
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.11.2014; Elsevier Limited
• Subjects: Accuracy; Algorithms; Alzheimer Disease - cerebrospinal fluid; Alzheimer Disease - diagnosis; Alzheimer's disease; Biomedical research; Block-wise missing data; Classification; Data Mining; Humans; Magnetic Resonance Imaging; Medical imaging; Multi-modal fusion; Multi-source; Neuroimaging - statistics & numerical data; NMR; Nuclear magnetic resonance; Optimization; Positron-Emission Tomography; Protein expression; Proteomics; Multi-source; Multi-modal fusion; Alzheimer's disease; Block-wise missing data; Optimization

Bio-imaging technologies allow scientists to collect large amounts of high-dimensional data from multiple heterogeneous sources for many biomedical applications. In the study of Alzheimer's Disease (AD), neuroimaging data, gene/protein expression data, etc., are often analyzed together to impro...