摘要
多视图聚类是无监督学习领域研究热点之一,近年来涌现出许多优秀的多视图聚类工作,但其中大多数方法均假设各视图是完整的,然而真实场景下数据收集过程极容易发生缺失,造成部分视图不完整。同时,很多方法采取传统机器学习方法(即浅层模型)对数据进行特征学习,这导致模型难以挖掘高维数据内的复杂信息。针对以上问题,本文提出一种面向不完整多视图聚类的深度互信息最大化方法。首先利用深度自编码器挖掘各视图深层次的隐含特征,并通过最大化潜在表示间的互信息来学习各视图间的一致性知识。然后,对于不完整视图中的缺失数据,利用多视图的公共潜在表示进行补全。此外,本文采用一种自步学习策略对网络进行微调,从易到难地学习数据集中的样本,得到更加宜于聚类的特征表示。最后,在多个真实数据集上进行实验,验证了本文方法的有效性。
Multi-view clustering is a research hotspot in the field of unsupervised learning.Of the many excellent multiview clustering studies that have recently arisen,most assume that each view is complete.However,in a real scene,the data are extremely easily missed in the collection process,resulting in partially incomplete views.Simultaneously,many methods use traditional machine learning,i.e.,the shallow-layer model,to learn data features,which makes it difficult for the model to mine the complex information of high-dimensional data.To solve these problems,in this paper,a novel deep mutual information maximization method is proposed for incomplete multi-view clustering.First,a deep autoencoder is used to learn the rich complex information of each view,and the knowledge of consistency among views is learned by the mutual information between potential representations.Then,the missing data are fixed up by the common latent representation of multi-view data.Additionally,this paper uses a self-paced strategy to fine-tune the model as it learns the samples from easy to difficult,obtaining a more clustering-friendly representation.Experiments performed on several real datasets show the effectiveness of our proposed method.
作者
张智慧
杨燕
张熠玲
ZHANG Zhihui;YANG Yan;ZHANG Yiling(School of Computing and Artificial Intelligence,Southwest Jiaotong University,Chengdu 611756,China)
出处
《智能系统学报》
CSCD
北大核心
2023年第1期12-22,共11页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金项目(61976247)。
关键词
数据挖掘
聚类
不完整多视图聚类
多视图表示学习
深度学习
自编码器
互信息
自步学习
data mining
clustering
incomplete multi-view clustering
multi-view representation learning
deep learning
autoencoder
mutual information
self-paced learning
