An adaptive latent factor model via particle swarm optimization for high-dimensional and sparse matrices
文献类型：会议论文
| 作者 | Chen, Sili1,2; Yuan, Ye1 ; Wang, Jin2
|
| 出版日期 | 2019 |
| 会议日期 | October 6, 2019 - October 9, 2019 |
| 会议地点 | Bari, Italy |
| DOI | 10.1109/SMC.2019.8914673 |
| 页码 | 1738-1743 |
| 英文摘要 | Latent factor (LF) models are greatly efficient in extracting valuable knowledge from High-Dimensional and Sparse (HiDS) matrices which are commonly seen in many industrial applications. Stochastic gradient descent (SGD) is an efficient scheme to build an LF model, yet its convergence rate depends vastly on the learning rate which should be tuned with care. Therefore, automatic selection of an optimal learning rate for an SGD-based LF model is a significant issue. To address it, this study incorporates the principle of particle swarm optimization (PSO) into an SGD-based LF model for searching an optimal learning rate automatically. With it, we further propose an adaptive Latent Factor (ALF) model. Empirical studies on two HiDS matrices from industrial applications indicate that an ALF model obviously outperforms an LF model in terms of convergence rate, and maintains competitive prediction accuracy for missing data. © 2019 IEEE. |
| 会议录 | 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
 |
| 语种 | 英语 |
| ISSN号 | 1062922X |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/9801]  |
| 专题 | 大数据挖掘及应用中心 |
| 作者单位 | 1.Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing; 400714, China 2.Computer School of China West Normal University, Nanchong, Sichuan; 637002, China; |
| 推荐引用方式 GB/T 7714 | Chen, Sili,Yuan, Ye,Wang, Jin. An adaptive latent factor model via particle swarm optimization for high-dimensional and sparse matrices[C]. 见:. Bari, Italy. October 6, 2019 - October 9, 2019. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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