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走向基于图形的多视图集群的统一框架

F Dornaika¹, S El Hajjar²

¹University of the Basque Country UPV/EHU, San Sebastian, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao, Spain; Ho Chi Minh City Open University, Ho Chi Minh City, Viet Nam.

Neural networks : the official journal of the International Neural Network Society

|February 29, 2024

概括

本研究介绍了一种新的一步多视图集群通过共识图学习和非负嵌入 (OSMGNE) 方法. 它通过学习共识相似度矩阵来有效处理杂的数据,改善多视图集群性能.

科学领域:

数据科学数据科学数据科学
机器学习机器学习
计算机视觉计算机视觉

背景情况:

多视图集群对于现实应用至关重要,常见的方法包括光谱集群,子空间方法,矩阵分解和内核方法.
现有的方法往往直接融合相似性矩阵,使它们易受噪声的影响,并将亲和学习与聚类分开.
这种限制可以在处理多个视图中的杂数据时降低性能.

研究的目的:

提出一种新的方法,即通过共识图学习和非负面嵌入 (OSMGNE) 进行一步多视图聚类,以解决现有的多视图聚类技术的局限性.
开发一种方法,通过学习共识相似度矩阵来稳健处理杂的相似度矩阵.
为了使多个矩阵的同时估计和没有超参数的自动视图加权.

主要方法:

通过共识图学习和非负嵌入 (OSMGNE) 方法引入了一步多视图集群.
开发了一个共识图的学习方法,以减轻单个视图相似度矩阵中的噪音.
集成的非负嵌入用于直接软集群分配,消除后处理步骤.
提出了一种代算法来解决该方法的两个变体的优化问题.

主要成果:

拟议的OSMGNE方法有效地学习共识相似度矩阵,减少噪音数据的影响.
非负嵌入允许直接生成集群赋值,简化了这个过程.
该方法共同估计多个矩阵 (相似性,光谱投影,指标) 并自动确定视图权重.
在真实数据集上的实验结果表明,拟议的方法优于现有方法.

结论:

新的OSMGNE方法为多视图聚类提供了强大而高效的解决方案,特别是在有噪音数据的情况下.
共同学习共识表示和集群分配可以提高整体集群准确性和稳定性.
该方法能够同时处理多个子任务并避免超参数,这使其成为一个实际的进步.

关键词:

数据融合数据融合基于图形的多视图集群.软集群的分配软集群的分配频谱嵌入是指光谱嵌入.统一和共识的学习图表学习.

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