拉普拉斯和阿克坦的一些理论结果惩罚了普通最小平方线性回归估计器
Majnu John1,2, Sujit Vettam3
1Departments of Mathematics and of Psychiatry, Hofstra University, Hempstead, NY.
概括
新拉普拉斯和arctan惩罚函数为高维统计提供了稀疏的模型. 这些非凸的处罚证明了近乎无偏见,并且满足了被处罚的普通最小平方回归中的预言属性.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 高维数据分析 高维数据分析
背景情况:
- 稀疏建模对于高维的统计问题至关重要.
- 非凸起的惩罚函数为传统方法提供了替代方案.
- 拉普拉斯 (Laplace) 和阿克坦 (Arctan) 罚款是最近对非凸正规化的新增.
研究的目的:
- 为了研究拉普拉斯和阿克坦的理论性质,惩罚了普通最小平方线性回归.
- 将这些新处罚与现有的桥梁处罚进行比较.
- 为了评估它们在稀疏模型选择中的表现.
主要方法:
- 对处罚最小平方模型的理论分析.
- 在正规和一般设计情况下进行检查.
- 以固定和不断增加的特征数和样本大小进行非对称分析.
主要成果:
- 证明了拉普拉斯和arctan处罚在正规情况下的非零回归权重的近乎无偏见.
- 在不同的非对称设置下为一般设计案例提供理论结果.
- 展示了拉普拉斯和阿克坦的惩罚都满足预言的属性.
- 与凸和非凸桥梁罚款相比,突出差异.
结论:
- 拉普拉斯和阿克坦的惩罚在理论上对稀疏的高维回归是合理的.
- 这些处罚表现出可取的属性,包括预言属性.
- 模拟结果表明实际实用性,特别是在梯度下降优化方面.
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