Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

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

【摘要】

：

Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various

【作者】

：

Mingsheng Shang Xin Luo Zhigan

【机构】

：

theChongqingEngineeringResearchCenterofBigDataApplicationforSmartCities,theSchoolofComputerSciencean

【出处】

：

自动化学报:英文版

【发表日期】

：

2019年1期

【关键词】

：

BIG data HIGH-DIMENSIONAL and SPARSE matrix LATENT FACTOR analysis LATENT FACTOR

【基金项目】

：

the National Natural Science Foundation of China (61772493,91646114),Chongqing research program of technology innovation and application (cstc2017rgzn-zdyfX0020),in part by the Pioneer Hundred Talents

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Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many

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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

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