基于独立自表达学习的不完全多视图聚类  

Incomplete multi-view clustering via independent self-representation learning

作　　者：诸葛文章 范瑞东 罗廷金 陶红 侯臣平[1] Wenzhang ZHUGE;Ruidong FAN;Tingjin LUO;Hong TAO;Chenping HOU(College of Science,National University of Defense Technology,Changsha 410073,China;Naval Research Institute,Beijing 100161,China)

机构地区：[1]国防科技大学理学院 [2]海军研究院

出　　处：《中国科学：信息科学》2022年第7期1186-1203,共18页Scientia Sinica(Informationis)

基　　金：国家自然科学基金(批准号:61922087,61906201,62006238);湖南省杰出青年自然科学基金(批准号:2019JJ20020)资助。

摘　　要：不完全多视图聚类是通过结合多视图数据的异构不完全特征来获得数据本征结构,从而提高聚类性能的一种学习范式.在实际应用中,各个视图除了缺失某些完整样本外,还会受到缺失值与异常值的影响,使得大部分传统的不完全多视图聚类方法失效.为解决上述问题,本文提出一种基于独立自表达学习的不完全多视图聚类方法.该方法通过自表达重构,补全缺失的特征的同时学习视图独有的自表达矩阵,然后为自表达矩阵添加低秩约束,更好地挖掘本征结构,并通过引入希尔伯特–施密特独立性准则来衡量不同视图间的差异性.多个数据集上的实验结果表明,所提方法在大多数情况下能取得较对比方法更优的聚类结果.Incomplete multi-view clustering is a learning paradigm that combines heterogeneous and incomplete characteristics of multi-view data to obtain data structure and improve clustering performance.In practical application,each view will also be affected by missing values and outliers,in addition to missing some complete samples,which makes most traditional incomplete multi-view clustering methods ineffective.An incomplete multiview clustering method based on independent self-presentation learning is proposed to solve the above issue.The proposed method fills in the missing features and learns the unique self-presentation matrix of each view using self-representation reconstruction.Then,to better mine the structure,low-rank constraints are added to the self-presentation matrix,and the diversity among different views is measured by introducing the Hilbert-Schmidt independence criterion.Experimental results on multiple data sets show that the proposed method can achieve better clustering than other advanced methods in most cases.

关 键 词：不完全多视图聚类 特征任意缺失 自表达 差异性 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]