A Distributed Adaptive Second-Order Latent Factor Analysis Model  

作　　者：Jialiang Wang Weiling Li Xin Luo 

机构地区：[1]the School of Computer Science and Technology,Dongguan University of Technology [2]the College of Computer and Information Science,Southwest University

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第11期2343-2345,共3页自动化学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China (62102086, 62272078);the Guangdong Basic and Applied Basic Research Foundation (2022A1515140102, 2021B1515140046);the Guangdong Province Universities and College Pearl River Scholar Funded Scheme (2019)。

摘　　要：Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a second-order optimizer has stronger ability in approaching a better solution when dealing with the non-convex optimization problems, thus obtaining better performance in extracting the latent factors(LFs) well representing the known information from high-dimensional and incomplete data.

关 键 词：REPRESENTATION INCOMPLETE APPROACHING 

分 类 号：O224[理学—运筹学与控制论]