Parameter-Free Shifted Laplacian Reconstruction for Multiple Kernel Clustering  

作　　者：Xi Wu Zhenwen Ren F.Richard Yu 

机构地区：[1]the School of National Defence Science and Technology,Southwest University of Science and Technology,Mianyang 621010,China [2]the School of National Defence Science and Technology,Southwest University of Science and Technology,Mianyang 621010 [3]the Guangxi Key Laboratory of Machine Vision and Intelligent Control,Wuzhou University,Wuzhou 543002 [4]the Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ),Shenzhen 518060,China

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第4期1072-1074,共3页自动化学报（英文版）

基　　金：Guangxi Key Laboratory of Machine Vision and Intelligent Control(2022B07);the Open Research Fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ)(GML-KF-22-04);the Natural Science Foundation of Southwest University of Science and Technology(22zx7101);the National Natural Science Foundation of China(62106209)。

摘　　要：Dear Editor,This letter proposes a parameter-free multiple kernel clustering(MKC)method by using shifted Laplacian reconstruction.Traditional MKC can effectively cluster nonlinear data,but it faces two main challenges:1)As an unsupervised method,it is up against parameter problems which makes the parameters intractable to tune and is unfeasible in real-life applications;2)Only considers the clustering information,but ignores the interference of noise within Laplacian.

关 键 词：PARAMETER LETTER PARAMETER 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]