共识多视图光谱集群网络,具有统一的相似性
Yang Zhao1, Daidai Zhu2, Aihong Yuan3
1School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University, Xi'an, 710072, China; China and Shanghai Artificial Intelligence Laboratory, China and Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China; National Key Laboratory of Air-based Information Perception and Fusion, Luoyang, 471099, China.
概括
这项研究引入了一种新的深度网络,用于多视图光谱聚类,增强共识表示学习. 该方法通过统一各个视图的相似性和使用对比学习对齐嵌入来提高集群性能.
科学领域:
- 计算机科学 计算机科学
- 机器学习 机器学习
- 数据挖掘 数据挖掘
背景情况:
- 多视图光谱聚类需要从异质数据中学习共识表示.
- 现有的方法经常单独构建亲和矩阵,限制了统一的相似性学习.
- 在嵌入表示中缺乏明确的一致性强制执行导致了次优集群.
研究的目的:
- 为有效的共识代表学习提出一个深度的多视角光谱集群网络.
- 解决统一相似性的局限性,并在现有方法中嵌入一致性.
- 通过增强的代表性学习来提高集群性能.
主要方法:
- 开发了一个深层次的光谱嵌入式学习框架,整合数据,以便在各个观点之间实现统一的相似性.
- 构建了一个光谱映射网络以提取常见的嵌入表示.
- 采用局部结构约束的对比学习来调整光谱嵌入表示,并强制执行一致性.
主要成果:
- 拟议的框架成功地学习了跨多个观点的统一相似性.
- 局部结构受约束的对比学习有效地调整了光谱嵌入.
- 在八个公共数据集上的比较实验证明了算法的优越性和有效性.
结论:
- 拟议的深度多视图光谱集群网络有效地学习共识表示.
- 统一相似性和对准嵌入式显著提高了集群性能.
- 该方法为多视图光谱聚类任务提供了一种优越的方法.
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