耐噪声共性和个性标签学习多视图多标签特征选择使用模糊的相互信息
IEEE transactions on cybernetics
|November 11, 2025
概括
本研究引入了一种新的多视图多标签特征选择方法. 它有效地整合了共识和互补信息,同时减轻了噪音,提高了准确性.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 人工智能的人工智能
背景情况:
- 多视图多标签功能选择对于处理大型复杂数据集至关重要.
- 现有的方法往往分散共识和互补信息,导致噪音和模两可.
- 目前的方法忽视了视图标签相关性,影响了视图权重的准确性.
研究的目的:
- 提出一个集成的多视图多标签功能选择方法.
- 为了解决噪声问题,并提高视图重量估计的准确性.
- 共同学习共同性和个性标签结构.
主要方法:
- 非负矩阵因子化为共同性标签矩阵.
- 模糊的相互信息用于视图权重.
- 噪声标签矩阵用于降低噪声.
- 基于缩模型的优化与收证明.
主要成果:
- 在多个基准数据集中证明了有效性.
- 改进了噪音处理和特征选择中的模两可.
- 通过考虑视图标签相关性来准确估计视图权重.
结论:
- 提出的方法有效地整合了共识和补充信息.
- 抗噪声和准确的视图权重提高了功能选择性能.
- 该方法为多视图多标签特征选择问题提供了强大的解决方案.
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