Sliced Inverse Regression with Adaptive Spectral Sparsity for Dimension Reduction

Engineering

• Sparsity 100%
• Spectral Sparsity 100%
• Computation Complexity 50%
• Feature Extraction 50%
• Dimensional Data 50%
• Pattern Detection 50%
• Microarrays 50%
• Feature Model 50%
• State-of-the-Art Method 50%
• Classification Task 50%
• Least Absolute Shrinkage and Selection Operator 50%
• Learning System 50%
• Regression Coefficient 50%

Computer Science

• Sparsity 100%
• Pattern Classification 66%
• Feature Selection 66%
• Computation Complexity 33%
• Subspace Projection 33%
• facial image 33%
• Pattern Detection 33%
• Research Effort 33%
• Regression Coefficient 33%
• Microarray Data 33%
• High Dimensional Data 33%
• Globally Optimal Solution 33%
• Classification Task 33%
• Feature Extraction 33%
• Machine Learning 33%
• Learning System 33%

Mathematics

• Regression Model 100%
• Dimensional Data 100%
• Projection Vector 100%
• Interpretation Ability 100%
• Regression Coefficient 100%
• Least Absolute Shrinkage and Selection Operator 100%