Non-negativity constrained missing data estimation for high-dimensional and sparse matrices
文献类型：会议论文
| 作者 | Luo, Xin2,3 ; Li, Shuai1
|
| 出版日期 | 2017 |
| 会议日期 | August 20, 2017 - August 23, 2017 |
| 会议地点 | Xi'an, China |
| DOI | 10.1109/COASE.2017.8256293 |
| 页码 | 1368-1373 |
| 英文摘要 | Latent factor (LF) models have proven to be accurate and efficient in extracting hidden knowledge from high-dimensional and sparse (HiDS) matrices. However, most LF models fail to fulfill the non-negativity constraints that reflect the non-negative nature of industrial data. Yet existing non-negative LF models for HiDS matrices suffer from slow convergence leading to considerable time cost. An alternating direction method-based non-negative latent factor (ANLF) model decomposes a non-negative optimization process into small sub-tasks. It updates each LF non-negatively based on the latest state of those trained before, thereby achieving fast convergence and maintaining high prediction accuracy and scalability. This paper theoretically analyze the characteristics of an ANLF model, and presents detailed empirical study regarding its performance on several HiDS matrices arising from industrial applications currently in use. Therefore, its capability of addressing HiDS matrices is validated in both theory and practice. © 2017 IEEE. |
| 会议录 | 13th IEEE Conference on Automation Science and Engineering, CASE 2017
 |
| 语种 | 英语 |
| 电子版国际标准刊号 | 21618089 |
| ISSN号 | 21618070 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/9774]  |
| 专题 | 大数据挖掘及应用中心 |
| 作者单位 | 1.Department of Computing, Hong Kong Polytechnic University, Hong Kong; 999077, Hong Kong 2.Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing; 400714, China; 3.Shenzhen Engineering Laboratory for Mobile Internet Application Middleware Technology, Shenzhen University, Shenzhen; 518060, China; |
| 推荐引用方式 GB/T 7714 | Luo, Xin,Li, Shuai. Non-negativity constrained missing data estimation for high-dimensional and sparse matrices[C]. 见:. Xi'an, China. August 20, 2017 - August 23, 2017. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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