基于邻域多核学习的后融合多视图聚类算法

Late Fusion Multi-View Clustering Based on Local Multi-Kernel Learning

基于图谱理论的多视图聚类是该领域的代表性方法之一.然而,现有模型尚存在3个问题.1)这类方法大多没有考虑不同视图之间的聚类性能差异,强制要求所有视图共享一个公共相似图;2)部分模型将相似图构建和聚类分步进行,导致所构建的相似图对于聚类任务并非最优;3)虽已有若干模型采用核学习处理数据间的非线性关系,但大多基于全局模型计算数据在核空间中的自表达关系,不利于充分挖掘局部非线性信息,且易带来沉重的计算负荷.为了应对以上问题,提出一种基于邻域多核学习的后融合多视图聚类算法,在类划分空间而不是数据相似图的层次进行信息融合,采用邻域多核学习方案在充分保留局部非线性关系的同时减轻计算负荷,并提出一种交替优化方案将相似图构建、多核组合、类指示矩阵生成等子任务在统一的框架下进行协同优化.多个数据集上的实验表明:该算法具有良好的多视图聚类效果.

Graph-based multi-view clustering is one of the representative methods in that field. However, existing models still have problems as following. First, most of them do not consider the difference of clustering capacity among different views and force all views to share a common similarity graph. Next, some models construct the similarity graph and conduct clustering in separated steps, resulting in the constructed similarity graph is not optimal for the following clustering tasks. Finally, although there are many models using kernel learning to deal with the nonlinear relationship between data points, most of them calculate the self-expressive relationship in kernel space based on global models. Such global schemes are not conducive to fully explore local nonlinear relationship, and easy to bring about heavy computing load. Therefore, this paper proposes a late fusion multi-view clustering model based on local multi-kernel learning. We implement information fusion at the level of class partition space rather than similarity graph, and adopt local multi-kernel learning scheme to fully preserve the local nonlinear relationship as well as reduce the computational load. We also propose an alternative optimization scheme to solve the construction of similarity graph, combination of multi-kernel and generation of class indicator matrix in a unified framework. Experiments on multiple datasets show that the proposed method has good multi-view clustering effect.