Kernel subspace learning for pattern classification

Computer Science

• Feature Vector 100%
• Pattern Classification 100%
• Approximation (Algorithm) 66%
• Learning Technique 66%
• Kernel Method 66%
• Feature Extraction 33%
• Computational Complexity 33%
• Dimensional Feature 33%
• Machine Learning Algorithm 33%
• Data Streaming 33%
• Nonlinear Transformation 33%
• Kernel Function 33%
• Memory Requirement 33%

Keyphrases

• Kernel Subspace Learning 100%
• Explicit Aspects 100%
• Nonparametric Weighted Feature Extraction 50%
• Modern Infrastructure 50%
• Linear Learning 50%
• Efficient Computing 50%