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5. Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data

Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data

来源 :自动化学报:英文版 | 被引量 : 0次 | 上传用户：lingyuehqu2009

【摘 要】

：

High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor ne

【作 者】

：

Di Wu Xin Luo 

【机 构】

：

theChongqingKeyLaboratoryofBigDataandIntelligentComputing,theChongqingSchool

【出 处】

：

自动化学报:英文版

【发表日期】

：

2021年4期

【关键词】

：

High-dimensional and sparse matrix L1-norm L2 norm latent factor model recommend 

【基金项目】

：

supported in part by the National Natural Science Foundation of China(61702475,61772493,61902370,62002337),in part by the Natural Science Foundation of Chongqing,China(cstc2019jcyj-msxmX0578,cstc2019j

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论文部分内容阅读

High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great signifi

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