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Fast Stochastic Ordinal Embedding With Variance Reduction and Adaptive Step Size

文献类型：期刊论文

作者Ma, Ke9,10; Zeng, Jinshan8; Xiong, Jiechao7; Xu, Qianqian6; Cao, Xiaochun3,4,5; Liu, Wei7; Yao, Yuan1,2
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2021-06-01
卷号33期号:6页码:2467-2478
关键词Ordinal embedding SVRG non-convex optimization barzilai-borwein (BB) step size
ISSN号1041-4347
DOI10.1109/TKDE.2019.2956700
英文摘要Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are based on semi-definite programming (SDP), which is generally time-consuming and degrades the scalability, especially confronting large-scale data. To overcome this challenge, we propose a stochastic algorithm called SVRG-SBB, which has the following features: i) achieving good scalability via dropping positive semi-definite (PSD) constraints as serving a fast algorithm, i.e., stochastic variance reduced gradient (SVRG) method, and ii) adaptive learning via introducing a new, adaptive step size called the stabilized Barzilai-Borwein (SBB) step size. Theoretically, under some natural assumptions, we show the O(1/T) rate of convergence to a stationary point of the proposed algorithm, where T is the number of total iterations. Under the further Polyak-Lojasiewicz assumption, we can show the global linear convergence (i.e., exponentially fast converging to a global optimum) of the proposed algorithm. Numerous simulations and real-world data experiments are conducted to show the effectiveness of the proposed algorithm by comparing with the state-of-the-art methods, notably, much lower computational cost with good prediction performance.
资助项目National Natural Science Foundation of China[61861166002] ; National Natural Science Foundation of China[61672514] ; National Natural Science Foundation of China[61976202] ; National Natural Science Foundation of China[61977038] ; National Natural Science Foundation of China[61603162] ; National Natural Science Foundation of China[61876074] ; Beijing Natural Science Foundation[4172068] ; Beijing Natural Science Foundation[4182079] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDB28000000] ; Youth Innovation Promotion Association CAS ; Hong Kong Research Grant Council (HKRGC)[16303817]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000649587600011
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/17705]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Cao, Xiaochun; Yao, Yuan
作者单位1.Hong Kong Univ Sci & Technol, Dept Comp Sci & Engn, Kowloon, Clear Water Bay, Hong Kong, Peoples R China
2.Hong Kong Univ Sci & Technol, Dept Math, Kowloon, Clear Water Bay, Hong Kong, Peoples R China
3.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing 100049, Peoples R China
4.Peng Cheng Lab, Cyberspace Secur Res Ctr, Shenzhen 518055, Peoples R China
5.Chinese Acad Sci, Inst Informat Engn, State Key Lab Informat Secur, Beijing 100093, Peoples R China
6.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
7.Tencent AI Lab, Shenzhen 518057, Guangdong, Peoples R China
8.Jiangxi Normal Univ, Sch Comp Informat Engn, Nanchang 330022, Jiangxi, Peoples R China
9.Peng Cheng Lab, Artificial Intelligence Res Ctr, Shenzhen 518055, Peoples R China
10.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Ma, Ke,Zeng, Jinshan,Xiong, Jiechao,et al. Fast Stochastic Ordinal Embedding With Variance Reduction and Adaptive Step Size[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2021,33(6):2467-2478.
APA Ma, Ke.,Zeng, Jinshan.,Xiong, Jiechao.,Xu, Qianqian.,Cao, Xiaochun.,...&Yao, Yuan.(2021).Fast Stochastic Ordinal Embedding With Variance Reduction and Adaptive Step Size.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,33(6),2467-2478.
MLA Ma, Ke,et al."Fast Stochastic Ordinal Embedding With Variance Reduction and Adaptive Step Size".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 33.6(2021):2467-2478.

入库方式： OAI收割

来源：计算技术研究所

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