Efficient Clustering Network Based on Matrix Factorization  

作　　者：Jieren Cheng Jimei Li Faqiang Zeng Zhicong Tao and Yue Yang 

机构地区：[1]School of Computer Science and Technology,Hainan University,Haikou,570228,China [2]School of Cyberspace Security,Hainan University,Haikou,570228,China [3]Hainan Blockchain Technology Engineering Research Center,Haikou,570228,China

出　　处：《Computers, Materials & Continua》2024年第7期281-298,共18页计算机、材料和连续体（英文）

基　　金：supported by the Key Research and Development Program of Hainan Province(Grant Nos.ZDYF2023GXJS163,ZDYF2024GXJS014);National Natural Science Foundation of China(NSFC)(Grant Nos.62162022,62162024);the Major Science and Technology Project of Hainan Province(Grant No.ZDKJ2020012);Hainan Provincial Natural Science Foundation of China(Grant No.620MS021);Youth Foundation Project of Hainan Natural Science Foundation(621QN211);Innovative Research Project for Graduate Students in Hainan Province(Grant Nos.Qhys2023-96,Qhys2023-95).

摘　　要：Contrastive learning is a significant research direction in the field of deep learning.However,existing data augmentation methods often lead to issues such as semantic drift in generated views while the complexity of model pre-training limits further improvement in the performance of existing methods.To address these challenges,we propose the Efficient Clustering Network based on Matrix Factorization(ECN-MF).Specifically,we design a batched low-rank Singular Value Decomposition(SVD)algorithm for data augmentation to eliminate redundant information and uncover major patterns of variation and key information in the data.Additionally,we design a Mutual Information-Enhanced Clustering Module(MI-ECM)to accelerate the training process by leveraging a simple architecture to bring samples from the same cluster closer while pushing samples from other clusters apart.Extensive experiments on six datasets demonstrate that ECN-MF exhibits more effective performance compared to state-of-the-art algorithms.

关 键 词：Contrastive learning CLUSTERING matrix factorization 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]