信息融合:机器学习方法:machine learning methods

1 Introduction 1.1 Why Do Information Fusion? 1.2 Related Works 1.2.1 Multi-View Based Fusion Methods 1.2.2 Multi-Technique Based Fusion Methods 1.3 Book Overview References 2 Information Fusion Based on Sparse/Collaborative Representation 2.1 Motivation and Preliminary 2.1.1 Motivation 2.1.2 Preliminary 2.2 Joint Similar and Specific Learning 2.2.1 _ Problem Formulation 2.2.2 Optimization for JSSL 2.2.3 The Classification Rule for JSSL 2.2.4 Experimental Results 2.2.5 Conclusion 2.3 Relaxed Collaborative Representation 2.3.1 Problem Formulation 2.3.2 Optimization for RCR 2.3.3 The Classification Rule for RCR 2.3.4 Experimental Results 2.3.5 Conclusion 2.4 Joint Discriminative and Collaborative Representation 2.4.1 Problem Formulation 2.4.2 Optimization for JDCR 2.4.3 The Classification Rule for JDCR 2.4.4 Experimental Results 2.4.5 Conclusion References 3 Information Fusion Based on Gaussian Process Latent Variable Model 3.1 Motivation and Preliminary 3.1.1 Motivation 3.1.2 Preliminary 3.2 Shared Auto-encoder Gaussian Process Latent Variable Model 3.2.1 Problem Formulation 3.2.2 Optimization for SAGP 3.2.3 Inference 3.2.4 Experimental Results 3.2.5 Conclusion 3.3 Multi-Kernel Shared Gaussian Process Latent Variable Model 3.3.1 Problem Formulation 3.3.2 Optimization for MKSGP 3.3.3 Inference 3.3.4 Experimental Results 3.3.5 Conclusion 3.4 Shared Linear Encoder-Based Multi-Kernel Gaussian Process Latent Variable Model 3.4.1 Problem Formulation 3.4.2 Optimization for SLEMKGP 3.4.3 Inference