本书主要论述信息融合技术及其应用，介绍不同技术的信息融合算法，包括基于稀疏/协作表示、高斯过程隐变量模型、多视角和多特征学习、贝叶斯模型、度量学习、权重分类器方法融合和深度学习等；讲述这些融合方法在图像分类、域自适应、人脸识别、疾病检测和图像检索等领域的应用，并使用多个数据库对上述方法的有效性和优越性进行了验证。
本书可供从事机器学习、计算机视觉、模式识别及生物度量等领域的研究人员、专业人士和研究生学习参考，也可为交叉学科的研究人员提供帮助。

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
3.4.4 Experimental Results
3.4.5 Conclusion
References
4 Information Fusion Based on Multi-View and Multi-Feature Learning
4.1 Motivation
4.2 Generative Multi-View and Multi-Feature Learning
4.2.1 Problem Formulation
4.2.2 Optimization for MVMFL
4.2.3 Inference for MVMFL
4.2.4 Experimental Results
4.2.5 Conclusion
4.3 Hierarchical Multi-View Multi-Feature Fusion
4.3.1 Problem Formulation
4.3.2 Optimization for HMMF
4.3.3 Inference for HMMF
4.3.4 Experimental Results
4.3.5 Conclusion
References
5 Information Fusion Based on Metric Learning
5.1 Motivation
5.2 Generalized Metric Swarm Learning
5.2.1 Problem Formulation
5.2.2 Optimization for GMSL
5.2.3 Solving with Model Modification
5.2.4 Representation of Pairwise Samples in Metric Swarm Space
5.2.5 Sample Pair Verification
5.2.6 Remarks
5.2.7 Experimental Results
5.2.8 Conclusion
5.3 Combined Distance and Similarity Measure
5.3.1 Problem Formulation
5.3.2 Optimization for CDSM
5.3.3 Kernelized CDSM
5.3.4 Experimental Results
5.3.5 Conclusion
References
6 Information Fusion Based on Score/Weight Classifier Fusion
6.1 Motivation
6.2 Adaptive Weighted Fusion Approach
6.2.1 Problem Formulation
6.2.2 Rationale and Advantages of AWFA
6.2.3 Experimental Results
6.2.4 Conclusion
6.3 Adaptive Weighted Fusion of Local Kernel Classifiers
6.3.1 FaLK-SVM
6.3.2 Adaptive Fusion of Local SVM Classifiers
6.3.3 Experimental Results
6.3.4 Conclusion
References
7 Information Fusion Based on Deep Learning
7.1 Motivation
7.2 Dual Asymmetric Deep Hashing Learning
7.2.1 Problem Formulation
7.2.2 Optimization for DADH
7.2.3 Inference for DADH
7.2.4 Experimental Results
7.2.5 Conclusion
7.3 Relaxed Asymmetric Deep Hashing Learning
7.3.1 Problem Formulation
7.3.2 Optimization for RADH
7.3.3 Inference for RADH
7.3.4 Implementation
7.3.5 Experimental Results
7.3.6 Conclusion
7.4 Joint Learning of Single-Image and Cross-Image Representat