增强多视图协同作用:通过利用波浪损失函数与共识和互补原则进行强有力的学习
A Quadir1, Mushir Akhtar1, M Tanveer1
1Department of Mathematics, Indian Institute of Technology Indore, Simrol, Indore, 453552, India.
概括
本研究介绍了Wave-MvSVM,这是一个新的多视图支持向量机 (SVM) 模型,它使用波浪损失来有效地结合共识和互补原则. Wave-MvSVM在各种数据集上展示了更好的稳定性和性能,优于现有的多视图学习方法.
科学领域:
- 机器学习 机器学习
- 模式识别 模式识别
- 数据科学数据科学数据科学
背景情况:
- 多视图学习 (MvL) 增强了使用多个数据视角的模型,重点关注视图一致性和视图差异性.
- 现有的多视图支持向量机 (SVM) 模型主要使用共识原则,经常忽视互补性,缺乏对噪音数据的稳定性.
研究的目的:
- 引入Wave-MvSVM,这是一个新的MvL框架,它结合了共识和互补原则.
- 在多视图数据集中增强对噪音,易出错和视图不一致的样本的模型稳定性.
- 为了提高学习,利用波损失 (W-loss) 函数的独特特性.
主要方法:
- 拟议的Wave-MvSVM框架使用了一种新的波浪损失 (W-loss) 函数,其特点是光滑性,不对称性和边界性.
- 它结合了视图一致性的视图之间共同规范化术语和自适应组合权重策略.
- 优化是通过梯度下降 (GD) 和乘数交替方向方法 (ADMM) 的组合来实现的.
主要成果:
- 波-MvSVM有效地利用了共识和互补原则,提供了更全面的学习过程.
- W-loss函数显著减轻了噪音和异常数据的影响,增强了模型稳定性和分类校准.
- 跨多种数据集的实证评估表明,Wave-MvSVM的表现优于现有的基准模型,证明了卓越的概括能力.
结论:
- 波-MvSVM为多视图学习挑战提供了强大而高效的解决方案,有效地解决了以前方法的局限性.
- 该模型的有效性进一步通过其在精神分裂症数据集上的实施来验证,展示了现实世界的适用性.
- 理论概括得到Rademacher复杂性分析的支持.
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