计算机工程与应用2024，Vol.60Issue(4)：211-219,9.DOI:10.3778/j.issn.1002-8331.2209-0338
基于对比学习的矢量化特征空间嵌入聚类
Vectorized Feature Space Embedded Clustering Based on Contrastive Learning
摘要
Abstract
The deep embedding clustering(DEC)algorithm only embeds data into a low-dimensional vectorized feature space by autoencoder with a single instance reconstruction for clustering,and ignores the relationship between different in-stances,which leads to the instances in the embedding space may not be well distinguished from each other.To address the above problems,vectorized feature space embedded clustering based on contrastive learning(VECCL)method is pro-posed.By contrastive learning to identify the dissimilarity between data instances in a way,features with homogeneous near and different far clustering semantics are extracted from the data and brought into DEC as prior knowledge to guide the autoencoder to initialize a low-dimensional clustering feature space with deep data information.At the same time,the entropy loss constructed by the soft classification label and the reconstruction loss of the autoencoder are introduced into the clustering loss function as a regularization term to jointly refine the clustering.Compared with the experimental re-sults of DEC method on datasets CIFAR10,CIFAR100 and STL10,ACC increaseds by 48.1,23.1 and 41.8 percentage points,NMI increaseds by 41.0,25.2 and 39.0 percentage points,and ARI increaseds by 45.4,16.4 and 41.8 percentage points,respectively.关键词
深度聚类/对比学习/自编码器/矢量化特征空间/嵌入聚类Key words
deep clustering/contrastive learning/autoencoder/vectorized feature space/embedding clustering分类
信息技术与安全科学引用本文复制引用
郑洋,吴永明,徐岸..基于对比学习的矢量化特征空间嵌入聚类[J].计算机工程与应用,2024,60(4):211-219,9.基金项目
国家自然科学基金(51505094) （51505094）
贵州省科学技术基金计划项目(ZK[2023]一般079). （ZK[2023]一般079）
