Multimodal Similarity Gaussian Process Latent Variable Model

doi:10.1109/TIP.2017.2713045

	Multimodal Similarity Gaussian Process Latent Variable Model
	Song, Guoli 1,2,3; Wang, Shuhui 2; Huang, Qingming 1,2,3; Tian, Qi 4
	2017-09-01
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	26 期号:9 页码:4168-4181
摘要	Data from real applications involve multiple modalities representing content with the same semantics from complementary aspects. However, relations among heterogeneous modalities are simply treated as observation-to-fit by existing work, and the parameterized modality specific mapping functions lack flexibility in directly adapting to the content divergence and semantic complicacy in multimodal data. In this paper, we build our work based on the Gaussian process latent variable model (GPLVM) to learn the non-parametric mapping functions and transform heterogeneous modalities into a shared latent space. We propose multimodal Similarity Gaussian Process latent variable model (m-SimGP), which learns the mapping functions between the intra-modal similarities and latent representation. We further propose multimodal distance-preserved similarity GPLVM (m-DSimGP) to preserve the intra-modal global similarity structure, and multimodal regularized similarity GPLVM (m-RSimGP) by encouraging similar/dissimilar points to be similar/dissimilar in the latent space. We propose m-DRSimGP, which combines the distance preservation in m-DSimGP and semantic preservation in m-RSimGP to learn the latent representation. The overall objective functions of the four models are solved by simple and scalable gradient decent techniques. They can be applied to various tasks to discover the nonlinear correlations and to obtain the comparable low-dimensional representation for heterogeneous modalities. On five widely used real-world data sets, our approaches outperform existing models on cross-modal content retrieval and multimodal classification.
关键词	Multimodal learning Gaussian processes similarity preservation
DOI	10.1109/TIP.2017.2713045
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61672497] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61650202] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61572488] ; National Natural Science Foundation of China[61429201] ; National Basic Research Program of China (973 Program)[2015CB351802] ; Key Research Program of Frontier Sciences of CAS[QYZDJ-SSW-SYS013] ; ARO[W911NF-15-1-0290] ; Faculty Research Gift Awards by NEC Laboratory of America ; Faculty Research Gift Awards by NEC Laboratory of Blippar
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000404288000006
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：36[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7108
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Shuhui
作者单位	1.Univ Chinese Acad Sci, Sch Comp & Control Engn, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 101408, Peoples R China 4.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
推荐引用方式 GB/T 7714	Song, Guoli,Wang, Shuhui,Huang, Qingming,et al. Multimodal Similarity Gaussian Process Latent Variable Model[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2017,26(9):4168-4181.
APA	Song, Guoli,Wang, Shuhui,Huang, Qingming,&Tian, Qi.(2017).Multimodal Similarity Gaussian Process Latent Variable Model.IEEE TRANSACTIONS ON IMAGE PROCESSING,26(9),4168-4181.
MLA	Song, Guoli,et al."Multimodal Similarity Gaussian Process Latent Variable Model".IEEE TRANSACTIONS ON IMAGE PROCESSING 26.9(2017):4168-4181.