多图聚类模型(Graph-based Multi-view Clustering, GMC)是一种专门设计用于处理多视图数据的聚类算法,它利用图结构来捕捉数据点之间的关系,并通过联合优化多个视图的图表示来达到更准确的聚类效果。 GMC算法的核心在于能够有效融合不同来源的信息,即使这些信息可能存在矛盾或不完整,也能从中提取出一致的聚类结构。 GMC...
SNcut: Single view Normalized cut MKC: Multi-view Kmeans Clustering MultiNMF: Multi-view clustering via Non-negative Matrix Factorization CoregSC: Co-regularized Spectral Clustering MSC: Multi-view Spectral Clustering ASMV: Adaptive Structure-based Multi-view clustering MGL: Multiple Graph Learning MC...
Multi-view graph-based clustering (MGC) aims to cluster multi-view data via a graph learning scheme, and has aroused widespread research interests in behavior detection, face recognition, and information retrieval in recent years. However, most of the existing MGC methods usually learn the affinity...
we propose a general Graph-based Multi-view Clustering (GMC) to tackle these problems. GMC takes the data graph matrices of all views and fuses them to generate a unified graph matrix. The unified graph matrix in turn improves the data graph matrix of each view, and also gives the final ...
模型:它提出了一种通用的基于图的多视图聚类方法来解决当前方法的上述局限性。 GMC自动对每个视图进行加权,联合学习每个视图的图和融合图,融合后直接生成最终的聚类。值得注意的是,每个视图图的学习和融合图的学习可以互相帮助。 算法:它提出了一种交替迭代优化算法来解决GMC问题,其中每个子问题都有一个最优解。
Graph Filter-based Multi-view Attributed Graph Clustering[C]//IJCAI. 2021: 2723-2729. 【背景】 本文主要做的的属性图的聚类问题,当前的针对于multi-view的图表示学习方法主要可以被分为两个类别: 将多视图的数据映射到一个图中,然后使用单视图的算法。 通过一些表示学习方法来获得节点embedding之后,然后使用...
【每日一读】GMC: Graph-Based Multi-View Clustering,Hello!ଘ(੭ˊᵕˋ)੭昵称:海轰标签:程序猿|C++选手|学生简介:因C语言结识编程,随后转入计算机专业,获得过国家奖学金,有幸在竞赛
Lin Z, Kang Z. Graph Filter-based Multi-view Attributed Graph Clustering[C]//IJCAI. 2021: 2723-2729. 摘要导读 由于图数据的快速发展,图聚类在整个研究领域受到了很大的关注。然而现有的图聚类方法存在两个缺陷:1)大部分方法无法同时利用属性信息和图结构信息。2)大部分方法都无法处理包含多个图和多个属性信...
Wen J, Yan K, Zhang Z, et al. Adaptive graph completion based incomplete multi-view clustering[J]. IEEE Transactions on Multimedia, 2020, 23: 2493-2504. 摘要导读 现有的不完整多视图聚类方法都致力于从现有的视图中获得共有的表示或图,但是忽略了缺失视图中的隐含信息,以及不同视图之间信息的不平衡...
For graph-based multi-view clustering, a critical issue is to capture consensus cluster structures via a two-stage learning scheme. Most current methods learn pairwise similarities between objects for each view independently, which is widely used in single view. However, the consensus information con...