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Extending generalized unsupervised manifold alignment

文献类型：期刊论文

作者Yin, Xiaoyi2,3; Cui, Zhen1; Chang, Hong2,3; Ma, Bingpeng3; Shan, Shiguang2,3,4
刊名SCIENCE CHINA-INFORMATION SCIENCES
出版日期2022-07-01
卷号65期号:7页码:18
ISSN号1674-733X
关键词unsupervised manifold alignment relaxed integer programming Frank Wolfe algorithm
DOI10.1007/s11432-019-3019-3
英文摘要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.
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
语种英语
出版者SCIENCE PRESS
WOS记录号WOS:000819789900002
源URL[http://119.78.100.204/handle/2XEOYT63/19514]  
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.Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
2.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, 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.

入库方式： OAI收割

来源：计算技术研究所

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