KMS Chongqing Institute of Green and Intelligent Technology, CAS
| Convergence analysis of an SLF-NMU algorithm for non-negative latent factor analysis on a high-dimensional and sparse matrix | |
Liu, Zhigang; Luo, Xin
| |
| 2019 | |
| 摘要 | Non-negative latent factor (NLF) models have been frequently applied to information extraction, pattern recognition, and community detection. An NLF model can well represent a high-dimensional and sparse (HiDS) matrix of non-negative data and efficiently acquire useful knowledge from it. A single latent factor-dependent, non-negative and multiplicative update (SLF-NMU) algorithm is highly efficient to build NLF model. However, its convergence ability on such a matrix is still unveiled in theory. This paper presents the convergence property of an SLF-NMU algorithm. We theoretically prove that it can guarantee its convergence to a Karush-Kuhn-Tucher (KKT) stationary point. Empirical studies on two HiDS matrices from practical applications indicate that an SLF-NMU algorithm make an NLF model to converge at a relatively steady state. © 2019 IEEE. |
| 语种 | 英语 |
| DOI | 10.1109/SMC.2019.8913926 |
| 会议(录)名称 | 2019 IEEE International Conference on Systems, Man and Cybernetics, SMC 2019
 |
| 页码 | 1750-1756 |
| 收录类别 | EI |
| 会议地点 | Bari, Italy |
| 会议日期 | October 6, 2019 - October 9, 2019 |
