Sparse discriminative feature selection for multi-class Alzheimer’s Disease classification

Keyphrases

• Alzheimer's Disease Classification 100%
• Alzheimer's Disease Diagnosis 50%
• Alzheimer's Disease Neuroimaging Initiative 25%
• Binary Classification 25%
• Classification Performance 25%
• Dimensionality Reduction 25%
• Discriminative Feature Selection 100%
• Feature Dimension 25%
• Feature Interpretability 25%
• Feature Selection 50%
• Feature Selection Methods 25%
• Feature Subspace 50%
• Fisher Linear Discriminant Analysis 25%
• High Dimension 25%
• Learning Research 25%
• Linear Regression Method 25%
• Locality Preserving Projection 25%
• Machine Learning 25%
• Multi-class 100%
• Multi-class Classification 25%
• Neuroimaging Studies 25%
• Noise-resistant 25%
• Overfitting 25%
• Regression Model 25%
• Regularized Least Square Regression 25%
• Regularized Linear Regression 25%
• Small Sample Size 25%
• Subspace Learning 100%
• Unified Framework 25%

Computer Science

• Binary Classification 20%
• Classification Performance 20%
• Classification Problem 20%
• Dimension Reduction Method 20%
• Discriminative Feature 100%
• Feature Extraction 100%
• Feature Selection 100%
• Fitting Problem 20%
• Global Information 40%
• High Dimensionality 20%
• Interpretability 20%
• Learning System 20%
• Least Squares Method 20%
• Linear Discriminant Analysis 20%
• Local Information 40%
• locality preserving projection 20%
• Machine Learning 20%
• Multi Class Classification 20%
• Multiclass Classification 20%
• Neuroimaging Study 20%
• Regression Method 20%
• Small Sample Size 20%
• Unified Framework 20%

Chemical Engineering

• Learning System 100%