An efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis
文献类型：期刊论文
| 作者 | Li,Qing1,2; Pang,Guansong3; Shang,Mingsheng2
|
| 刊名 | Journal of Big Data
 |
| 出版日期 | 2022-07-16 |
| 卷号 | 9期号:1 |
| 关键词 | Big data analysis Latent factor analysis Simulated annealing Differential evolution algorithm Multi-parameter adaptive |
| DOI | 10.1186/s40537-022-00638-8 |
| 通讯作者 | Shang,Mingsheng(msshang@cigit.ac.cn) |
| 英文摘要 | AbstractA high-dimensional and incomplete (HDI) matrix is a typical representation of big data. However, advanced HDI data analysis models tend to have many extra parameters. Manual tuning of these parameters, generally adopting the empirical knowledge, unavoidably leads to additional overhead. Although variable adaptive mechanisms have been proposed, they cannot balance the exploration and exploitation with early convergence. Moreover, learning such multi-parameters brings high computational time, thereby suffering gross accuracy especially when solving a bilinear problem like conducting the commonly used latent factor analysis (LFA) on an HDI matrix. Herein, an efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis (ADMA) is proposed to address these problems. First, a periodic equilibrium mechanism is employed using the physical mechanism annealing, which is embedded in the mutation operation of differential evolution (DE). Then, to further improve its efficiency, we adopt a probabilistic evaluation mechanism consistent with the crossover probability of DE. Experimental results of both adaptive and non-adaptive state-of-the-art methods on industrial HDI datasets illustrate that ADMA achieves a desirable global optimum with reasonable overhead and prevails competing methods in terms of predicting the missing data in HDI matrices. |
| 语种 | 英语 |
| WOS记录号 | BMC:10.1186/S40537-022-00638-8 |
| 出版者 | Springer International Publishing |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/15943]  |
| 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 University of Posts and Telecommunications; College of Computer Science and Technology 2.Chinese Academy of Sciences; Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology 3.Singapore Management University; School of Computing and Information Systems |
| 推荐引用方式 GB/T 7714 | Li,Qing,Pang,Guansong,Shang,Mingsheng. An efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis[J]. Journal of Big Data,2022,9(1). |
| APA | Li,Qing,Pang,Guansong,&Shang,Mingsheng.(2022).An efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis.Journal of Big Data,9(1). |
| MLA | Li,Qing,et al."An efficient annealing-assisted differential evolution for multi-parameter adaptive latent factor analysis".Journal of Big Data 9.1(2022). |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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