摘要
针对深度子空间聚类问题中不同层次特征中互补信息挖掘困难的问题,在深度自编码器的基础上,提出了一种在编码器获取的低层和高层特征之间探索互补信息的多样性表示的深度子空间聚类(DRDSC)算法。首先,基于希尔伯特-施密特独立性准则(HSIC)建立了不同层次特征衡量多样性表示模型;其次,在深度自编码器网络结构中引入特征多样性表示模块,从而挖掘有利于提升聚类效果的图像特征;此外,更新了损失函数的形式,有效融合了多层次表示的底层子空间;最后,在常用的聚类数据集上进行了多次实验。实验结果表明,DRDSC在数据集Extended Yale B、ORL、COIL20和Umist上的聚类错误率分别达到1.23%、10.50%、1.74%和17.71%,与高效稠密子空间聚类(EDSC)相比,分别降低了10.41、16.75、13.12和12.92个百分点;与深度子空间聚类(DSC)相比,分别降低了1.44、3.50、3.68和9.17个百分点,说明所提出的DRDSC算法有更好的聚类效果。
Focusing on the challenge task for mining complementary information in different levels of features in the deep subspace clustering problem,based on the deep autoencoder,by exploring complementary information between the lowlevel and high-level features obtained by the encoder,a Diversity Represented Deep Subspace Clustering(DRDSC)algorithm was proposed.Firstly,based on Hilbert-Schmidt Independence Criterion(HSIC),a diversity representation measurement model was established for different levels of features.Secondly,a feature diversity representation module was introduced into the deep autoencoder network structure,which explored image features beneficial to enhance the clustering effect.Furthermore,the form of loss function was updated to effectively fuse the underlying subspaces of multi-level representation.Finally,several experiments were conducted on commonly used clustering datasets.Experimental results show that on the datasets Extended Yale B,ORL,COIL20 and Umist,the clustering error rates of DRDSC reach 1.23%,10.50%,1.74% and 17.71%,respectively,which are reduced by 10.41,16.75,13.12 and 12.92 percentage points,respectively compared with those of Efficient Dense Subspace Clustering(EDSC),and are reduced by 1.44,3.50,3.68 and 9.17 percentage points,respectively compared with Deep Subspace Clustering(DSC),which indicates that the proposed DRDSC algorithm has better clustering effect.
作者
马志峰
于俊洋
王龙葛
MA Zhifeng;YU Junyang;WANG Longge(College of Software,Henan University,Kaifeng Henan 475004,China;Henan Province Intelligent Data Processing Engineering Research Center(Henan University),Kaifeng Henan 475004,China)
出处
《计算机应用》
CSCD
北大核心
2023年第2期407-412,共6页
journal of Computer Applications
基金
河南省科技攻关项目(212102210078,222102210229)。
关键词
希尔伯特-施密特独立性准则
自编码器
相似度矩阵
谱聚类
子空间聚类
Hilbert-Schmidt Independence Criterion(HSIC)
autoencoder
similarity matrix
spectral clustering
subspace clustering
