规范化的最佳运输层,用于通用化的全球聚合操作
IEEE transactions on pattern analysis and machine intelligence
|September 12, 2023
概括
本研究介绍了一种使用最佳运输的通用全球聚合框架,提高机器学习性能. 新的规范化最佳运输聚合 (ROTP) 层提供了更好的数据表示,并降低了设计复杂性.
科学领域:
- 机器学习 机器学习
- 最佳运输理论 最佳运输理论
- 数据表示 数据表示
背景情况:
- 全球聚合对机器学习至关重要,但往往缺乏数学严谨性,导致性能不佳.
- 现有的聚合方法依赖于经验机制,而不是坚实的理论基础.
研究的目的:
- 开发一种基于最佳运输理论的全新,通用的全球聚合框架.
- 为信息融合和结构化数据表示提供可解释和数学上合理的方法.
主要方法:
- 开发了一个使用最佳运输的通用全球聚合框架,可通过期望最大化进行解释.
- 作为规范化最佳运输 (ROT) 的特殊案例,展示了现有的聚合方法.
- 引入可学习的规范化最佳运输聚合 (ROTP) 层,作为深层隐性层实施.
主要成果:
- 展示了ROTP层模仿现有方法或创建新的数据合适的聚合层的能力.
- 跨多实例学习,图形分类,图形集表示和图像分类的实验验证.
- ROTP层简化了全球聚合操作的设计和选择.
结论:
- 提出的基于运输的最佳框架为全球聚合提供了一个数学上坚实的基础.
- ROTP层为各种设置级机器学习任务提供了多功能和有效的解决方案.
- 这种方法提高了性能,并减少了全球聚合实施的复杂性.
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