Shared Linear Encoder Based Multi-Kernel Gaussian Process Latent Variable Model for Visual Classification

Keyphrases

• Latent Variables 100%
• Linear Encoder 100%
• Multiple Kernels 100%
• Visual Classification 100%
• Gaussian Process Latent Variable Model (GPLVM) 100%
• Covariance Matrix 75%
• Gaussian Process Prior 50%
• Encoder 25%
• Real-time Database 25%
• Radial Basis Function 25%
• Excellent Performance 25%
• Kernel Function 25%
• Linear Projection 25%
• Data Representation 25%
• Matrix Calculation 25%
• Multi-view Learning 25%
• Manifold Space 25%
• Discriminative Prior 25%
• Multiple Observations 25%
• Multi-view Learning Method 25%
• Single View 25%
• View-based Approach 25%

Computer Science

• Linear Encoders 100%
• Gaussian Kernel 100%
• Latent Variable Model 100%
• Covariance Matrix 75%
• Kernel Function 25%
• Radial Basis Function 25%
• Presented Approach 25%
• Outstanding Performance 25%
• Multiview Learning Method 25%
• Experimental Result 25%
• Data Representation 25%
• Multiple Observation 25%

Mathematics

• Gaussian Process 100%
• Latent Variable Model 100%
• Covariance Matrix 50%
• Manifold 16%
• Superiority 16%
• Linear Projection 16%
• Basis Function 16%