Semi-supervised hierarchical multimodal feature and sample selection for Alzheimer’s disease diagnosis

Mathematics

• Alzheimer's Disease 100%
• Sample Selection 100%
• Feature Selection 95%
• Magnetic Resonance Imaging 34%
• Single nucleotide Polymorphism 34%
• Neuroimaging 33%
• Learning Process 32%
• Leverage 30%
• Support Vector Machine 29%
• Ambiguous 29%
• Redundancy 26%
• Training 25%
• Preservation 24%
• Outlier 23%
• Demonstrate 14%
• Performance 14%
• Framework 13%
• Subset 13%
• Model 7%

Engineering & Materials Science

• Feature extraction 54%
• Neurodegenerative diseases 35%
• Neuroimaging 31%
• Polymorphism 29%
• Nucleotides 29%
• Labeled data 27%
• Magnetic resonance 24%
• Redundancy 20%
• Support vector machines 19%
• Imaging techniques 18%
• Data storage equipment 14%