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A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices

文献类型：期刊论文

作者Yuan, Ye1,2; He, Qiang3; Luo, Xin1,4,5; Shang, Mingsheng1
刊名IEEE TRANSACTIONS ON BIG DATA
出版日期2022-06-01
卷号8期号:3页码:784-794
关键词Computational modeling Sparse matrices Big Data Data models Stochastic processes Training Software algorithms Big data latent factor analysis generally multilayered structure deep forest multilayered extreme learning machine randomized-learning high-dimensional and sparse matrix stochastic gradient descent randomized model
ISSN号2332-7790
DOI10.1109/TBDATA.2020.2988778
通讯作者Luo, Xin(luoxin21@cigit.ac.cn) ; Shang, Mingsheng(msshang@cigit.ac.cn)
英文摘要How to extract useful knowledge from a high-dimensional and sparse (HiDS) matrix efficiently is critical for many big data-related applications. A latent factor (LF) model has been widely adopted to address this problem. It commonly relies on an iterative learning algorithm like stochastic gradient descent. However, an algorithm of this kind commonly consumes many iterations to converge, resulting in considerable time cost on large-scale datasets. How to accelerate an LF model's training process without accuracy loss becomes a vital issue. To address it, this study innovatively proposes a multilayered-and-randomized latent factor (MLF) model. Its main idea is two-fold: a) adopting randomized-learning to train LFs for implementing a 'one-iteration' training process for saving time; and 2) adopting the principle of a generally multilayered structure as in a deep forest or multilayered extreme learning machine to structure its LFs, thereby enhancing its representative learning ability. Empirical studies on six HiDS matrices from real applications demonstrate that compared with state-of-the-art LF models, an MLF model achieves significantly higher computational efficiency with satisfactory prediction accuracy. It has the potential to handle LF analysis on a large scale HiDS matrix with real-time requirements.
资助项目National Natural Science Foundation of China[61772493] ; National Natural Science Foundation of China[91646114] ; National Natural Science Foundation of China[51609229] ; National Natural Science Foundation of China[61872065] ; Natural Science Foundation of Chongqing (China)[cstc2019jcyjjqX0013] ; Pioneer Hundred Talents Program of Chinese Academy of Sciences
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000795107500016
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.138/handle/2HOD01W0/16251]  
专题中国科学院重庆绿色智能技术研究院
通讯作者Luo, Xin; Shang, Mingsheng
作者单位1.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Swinburne Univ Technol, Sch Software & Elect Engn, Melbourne, Vic 3122, Australia
4.Hong Kong Polytech Univ, Kowloon, Hong Kong 999077, Peoples R China
5.Cloudwalk, Dept Big Data Anal Tech, Hengrui Chongqing Artificial Intelligence Res Ctr, Chongqing 401331, Peoples R China
推荐引用方式
GB/T 7714		 Yuan, Ye,He, Qiang,Luo, Xin,et al. A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices[J]. IEEE TRANSACTIONS ON BIG DATA,2022,8(3):784-794.
APA Yuan, Ye,He, Qiang,Luo, Xin,&Shang, Mingsheng.(2022).A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices.IEEE TRANSACTIONS ON BIG DATA,8(3),784-794.
MLA Yuan, Ye,et al."A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices".IEEE TRANSACTIONS ON BIG DATA 8.3(2022):784-794.

入库方式： OAI收割

来源：重庆绿色智能技术研究院

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