在张量积空间的回归通过法
1Department of Biostatistics, University of Washington, Seattle, USA.
概括
我们介绍了用于多变量回归的估计器,解决了高维数据的挑战. 这些方法提供了统计和计算优势,有效地处理复杂的特征关系和稀疏性.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 条件平均值估计在统计学中至关重要.
- 非参数方法面临着高维度的挑战 (维度的诅咒).
- 对于大型特征集,现有的方法可能在统计和计算上是密集的.
研究的目的:
- 在多变量产品空间中开发有效的回归估计器.
- 为了解决高维设置中的经典非参数方法的局限性.
- 提供适用于各种复制内核希尔伯特空间的框架.
主要方法:
- 在多变量产品空间中对估计.
- 使用索博列夫类型的光滑空间作为一个例子.
- 开发一个复制内核希尔伯特空间的一般框架.
主要成果:
- 提出的估计器可以接受多变量回归.
- 这些方法有助于减轻维度的诅咒.
- 估计器表现出有利的统计和计算特性.
- 该框架有效地利用了特征稀疏性.
结论:
- 估计器为高维非参数回归提供了一个实用的解决方案.
- 这种方法提供了灵活性和可处理性之间的平衡.
- 这些方法是可适应的,可以结合额外的数据结构,如稀疏性.
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