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	Nonnegative Latent Factor Analysis-Incorporated and Feature-Weighted Fuzzy Double $c$-Means Clustering for Incomplete Data 期刊论文 IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2022, 卷号: 30, 期号: 10, 页码: 4165-4176 作者:  Song, Yan;  Li, Ming;  Zhu, Zhengyu;  Yang, Guisong;  Luo, Xin 收藏  |  浏览/下载：51/0  |  提交时间：2022/12/26 Big data  clustering  fuzzy double c-means  incomplete data  latent factor analysis  local feature weights.
	Convergence Analysis of Single Latent Factor-Dependent, Nonnegative, and Multiplicative Update-Based Nonnegative Latent Factor Models 期刊论文 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 卷号: 32, 期号: 4, 页码: 1737-1749 作者:  Liu, Zhigang;  Luo, Xin;  Wang, Zidong 收藏  |  浏览/下载：152/0  |  提交时间：2021/05/17 Manganese  Convergence  Computational modeling  Learning systems  Analytical models  Sparse matrices  Big Data  Big data  convergence  high-dimensional and sparse (HiDS) matrix  latent factor (LF) analysis  learning system  neural networks  nonnegative LF (NLF) analysis  single LF-dependent nonnegative and multiplicative update (SLF-NMU)
	An inherently nonnegative latent factor model for high-dimensional and sparse matrices from industrial applications 期刊论文 IEEE Transactions on Industrial Informatics, 2018, 卷号: 14, 期号: 5, 页码: 2011-2022 作者:  Luo, Xin;  Zhou, Mengchu;  Li, Shuai;  Shang, Mingsheng Adobe PDF(805Kb)  |  收藏  |  浏览/下载：415/0  |  提交时间：2019/06/26