Extending generalized unsupervised manifold alignment

doi:10.1007/s11432-019-3019-3

	Extending generalized unsupervised manifold alignment
	Yin, Xiaoyi 1,2; Cui, Zhen 4; Chang, Hong 1,2; Ma, Bingpeng 2; Shan, Shiguang 1,2,3
	2022-07-01
发表期刊	SCIENCE CHINA-INFORMATION SCIENCES
ISSN	1674-733X
卷号	65 期号:7 页码:18
摘要	Building connections between different data sets is a fundamental task in machine learning and related application community. With proper manifold alignment, the correspondences between data sets will assist us with comprehensive study of data processes and analyses. Despite the several progresses in semi-supervised and unsupervised scenarios, potent manifold alignment methods in generalized and realistic circumstances remain in absence. Besides, theretofore unsupervised algorithms seldom prove themselves mathematically. In this paper, we devise an efficient method to properly solve the unsupervised manifold alignment problem and denominate it as extending generalized unsupervised manifold alignment (EGUMA) method. More specifically, an explicit relaxed integer programming method is adopted to solve the unsupervised manifold alignment problem, which reconciles three factors covering the updated local structure matching, the the feature comparability and geometric preservation. An additional effort is retained on extending the Frank Wolfe algorithm to tacking our optimization problem. Besides our previous endeavors we adopt a new strategy for neighborhood discovery in the manifolds. The main advantages over previous methods accommodate (1) simultaneous alignment and discovery of manifolds; (2) complete unsupervised learning structure without any prerequisite correspondence; (3) more concise local geometry for the embedding space; (4) efficient alternative optimization; (5) strict mathematical analysis on the convergence and efficiency issues. Experiments on real-world applications verify the high accuracy and efficiency of our proposed method.
关键词	unsupervised manifold alignment relaxed integer programming Frank Wolfe algorithm
DOI	10.1007/s11432-019-3019-3
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61876171] ; National Natural Science Foundation of China[61976203] ; Shenzhen Institute of Artificial Intelligence and Robotics for Society[AC01202005015]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS记录号	WOS:000819789900002
出版者	SCIENCE PRESS
引用统计	被引频次：1[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/2XEOYT63/19515
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Chang, Hong
作者单位	1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China 4.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
推荐引用方式 GB/T 7714	Yin, Xiaoyi,Cui, Zhen,Chang, Hong,et al. Extending generalized unsupervised manifold alignment[J]. SCIENCE CHINA-INFORMATION SCIENCES,2022,65(7):18.
APA	Yin, Xiaoyi,Cui, Zhen,Chang, Hong,Ma, Bingpeng,&Shan, Shiguang.(2022).Extending generalized unsupervised manifold alignment.SCIENCE CHINA-INFORMATION SCIENCES,65(7),18.
MLA	Yin, Xiaoyi,et al."Extending generalized unsupervised manifold alignment".SCIENCE CHINA-INFORMATION SCIENCES 65.7(2022):18.