信息融合：机器学习方法(英文版) Information Fusion: Mac

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