Adjusted stochastic gradient descent for latent factor analysis
文献类型：期刊论文
| 作者 | Li, Qing1,2; Xiong, Diwen2; Shang, Mingsheng2
|
| 刊名 | INFORMATION SCIENCES
 |
| 出版日期 | 2022-04-01 |
| 卷号 | 588页码:196-213 |
| 关键词 | Big data analysis High-dimensional and incomplete matrix Stochastic gradient descent Latent factor analysis Gradient adjustment Adaptive model Particle swarm optimization Local optima |
| ISSN号 | 0020-0255 |
| DOI | 10.1016/j.ins.2021.12.065 |
| 通讯作者 | Shang, Mingsheng(msshang@cigit.ac.cn) |
| 英文摘要 | A high-dimensional and incomplete (HDI) matrix is a common form of big data in most industrial applications. Stochastic gradient descent (SGD) algorithm optimized latent factor analysis (LFA) model is often adopted in learning the abundant knowledge in HDI matrix. Despite its computational tractability and scalability, when solving a bilinear problem such as LFA, the regular SGD algorithm tends to be stuck in a local optimum. To address this issue, the paper innovatively proposes an Adjusted Stochastic Gradient Descent (ASGD) for Latent Factor Analysis, where the adjustment mechanism is implemented by considering the bi-polar gradient directions during optimization, such mechanism is theoretically proved for its efficiency in overstepping local saddle points and avoiding premature convergence. Also, the hyper-parameters of the model are implemented in a self-adaptive manner using the particle swarm optimization (PSO) algorithm, for higher practicality. Experimental results show that the proposed model outperforms other state-of-the-art approaches on six different HDI matrices from industrial applications, especially in prediction accuracy for missing data.(c) 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000768300300011 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/15627]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Shang, Mingsheng |
| 作者单位 | 1.Chongqing Univ Posts & Telecommun, Coll Comp Sci & Technol, Chongqing 400065, Peoples R China 2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Qing,Xiong, Diwen,Shang, Mingsheng. Adjusted stochastic gradient descent for latent factor analysis[J]. INFORMATION SCIENCES,2022,588:196-213. |
| APA | Li, Qing,Xiong, Diwen,&Shang, Mingsheng.(2022).Adjusted stochastic gradient descent for latent factor analysis.INFORMATION SCIENCES,588,196-213. |
| MLA | Li, Qing,et al."Adjusted stochastic gradient descent for latent factor analysis".INFORMATION SCIENCES 588(2022):196-213. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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