A Sparse Representation Model Using the Complete Marginal Fisher Analysis Framework and Its Applications to Visual Recognition

Keyphrases

• Visual Recognition 100%
• Marginal Fisher Analysis 100%
• Sparse Representation Model 100%
• Null Space 50%
• Recognition Task 50%
• Sparse Representation 50%
• Computational Efficiency 25%
• Dictionary 25%
• Sampling Methods 25%
• Local Sample 25%
• Caltech-256 25%
• Classification Accuracy 25%
• Extracting Features 25%
• Representative Dataset 25%
• Face Database 25%
• Discriminatory Power 25%
• Discriminatory Features 25%
• Object Categories 25%
• Transform Matrix 25%
• Purging 25%
• Yale 25%
• Column Space 25%
• Indoor Scene 25%
• Caltech101 25%
• Scene Categories 25%
• Discriminative Sparse Representation 25%
• Screening Rules 25%
• Discriminative Criterion 25%
• Convergence Issues 25%
• Class Scatter Matrices 25%
• Dictionary Selection 25%
• Larger Step Size 25%

Computer Science

• Sparse Representation 100%
• Analysis Framework 100%
• Representation Model 100%
• Computational Efficiency 20%
• Object Category 20%
• Discriminatory Power 20%
• Class Scatter Matrix 20%

Engineering

• Marginals 100%
• Tasks 50%
• Null Space 50%
• Computational Efficiency 25%
• Step Size α 25%
• Class Scatter Matrix 25%
• Column Space 25%