Generalized Robust Regression for Jointly Sparse Subspace Learning

Keyphrases

• Jointly Sparse 100%
• Robust Regression 100%
• Sparse Subspace Learning 100%
• Ridge Regression 50%
• Loss Function 33%
• L1-norm 33%
• Feature Selection 16%
• Regularization Term 16%
• Face Recognition 16%
• Feature Classification 16%
• Regression Method 16%
• Image Feature Extraction 16%
• Iterative Algorithm 16%
• Robustness to Outliers 16%
• Local Geometric Structure 16%
• Small-class Problem 16%
• Joint Sparsity 16%
• Norm Penalty 16%
• High-dimensional Case 16%
• Loss Regularization 16%
• Effective Features Selection 16%
• Image Feature Recognition 16%
• Variable Data 16%

Computer Science

• Ridge Regression 100%
• Feature Selection 66%
• Feature Extraction 33%
• image feature 33%
• Regularization Term 33%
• Face Recognition 33%
• Regression Method 33%
• Iterative Algorithm 33%
• Joint Sparsity 33%
• Feature Recognition 33%
• Dimensional Case 33%
• Generalized Function 33%

Engineering

• Feature Extraction 100%
• Loss Function 66%
• Joints (Structural Components) 33%
• Sparsity 33%
• Regularization 33%
• Effective Feature 33%
• Class Problem 33%
• Iterative Algorithm 33%
• Dimensional Case 33%