对于基于矩阵的雷尼 Entropy 的尼斯特罗姆意识的近似
Tieliang Gong1, Wen Wen1, Yuxin Dong1
1School of Computer Science of Technology, Xi'an Jiaotong University, Xi'an, China.
概括
我们为基于矩阵的Renni开发了高效的Nyström意识的近似方法.
科学领域:
- 信息理论 信息理论
- 机器学习 机器学习
- 数字分析 数字分析
背景情况:
- 基于矩阵的雷尼的α顺序对于信息理论学习有价值.
- 精确计算具有极高的时间复杂性,限制了其在大规模机器学习中的使用.
- 像Hutch++这样的现有近似需要对数据进行多次传递.
研究的目的:
- 提出基于矩阵的雷尼 Entropy 的新尼斯特罗姆意识的近似策略.
- 为了减少计算复杂性,同时保持准确性.
- 改进现有的近似方法,如Hutch++.
主要方法:
- 开发了基于任意α级矩阵的雷尼 Entropy 的 Nyström-aware 素描技术.
- 将时间复杂性减少到O ((n2s) 使用s查询的随机向量.
- 专注于对称的正半定义矩阵.
主要成果:
- 与精确方法相比,实现了计算复杂性的显著降低.
- 与基于Hutch++的近似方法相比,经过较少的数据传递,证明了更高的效率.
- 查询复杂度在近似误差方面被证明是接近最佳的.
- 启用了并行计算的潜力.
结论:
- 尼斯特罗姆意识的近似提供了一个基于矩阵的雷尼的计算效率高和准确的替代方案.
- 建议的方法在速度和数据效率方面优于现有技术.
- 这些近似值适用于大规模的机器学习应用.
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