Classification of weak multi-view signals by sharing factors in a mixture of Bayesian group factor analyzers

Computer Science

• Models 100%
• Classification 100%
• Analysis Model 66%
• Motivation 33%
• Classification Accuracy 33%
• Noise-to-Signal Ratio 33%
• Multi-View Learning 33%
• Sensor Measurement 33%
• Classification Models 33%
• Auxiliary Information 33%
• Class Distribution 33%
• Trial Functional 33%
• Learning Group 33%
• Functional Brain Imaging 33%

Neuroscience

• Magnetoencephalography 66%
• Brain Imaging 33%
• Electroencephalography 33%
• Motivation 33%
• Signal-to-Noise Ratio 33%
• Functional Magnetic Resonance Imaging 33%