...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mechanical systems and signal processing >Robust satisficing linear regression: Performance/robustness trade-off and consistency criterion
【24h】

Robust satisficing linear regression: Performance/robustness trade-off and consistency criterion

机译：稳健的线性回归：性能/稳健性的权衡和一致性标准

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Linear regression quantifies the linear relationship between paired sets of input and output observations. The well known least-squares regression optimizes the performance criterion defined by the residual error, but is highly sensitive to uncertainties or perturbations in the observations. Robust least-squares algorithms have been developed to optimize the worst case performance for a given limit on the level of uncertainty, but they are applicable only when that limit is known. Herein, we present a robust-satisficing approach that maximizes the robustness to uncertainties in the observations, while satisficing a critical sub-optimal level of performance. The method emphasizes the trade-off between performance and robustness, which are inversely correlated. To resolve the resulting trade-off we introduce a new criterion, which assesses the consistency between the observations and the linear model. The proposed criterion determines a unique robust-satisficing regression and reveals the underlying level of uncertainty in the observations with only weak assumptions. These algorithms are demonstrated for the challenging application of linear regression to neural decoding for brain-machine interfaces. The model-consistent robust-satisfying regression provides superior performance for new observations under both similar and different conditions.
机译：线性回归量化输入和输出观测值的成对集合之间的线性关系。众所周知的最小二乘回归可优化由残差定义的性能标准，但对观测结果的不确定性或扰动高度敏感。已经开发了鲁棒的最小二乘算法，以针对不确定性水平的给定限制优化最坏情况下的性能，但是它们仅在已知该限制时适用。在本文中，我们提出了一种鲁棒性令人满意的方法，该方法最大程度地提高了对观测结果不确定性的鲁棒性，同时满足了关键的次优性能水平。该方法强调性能和鲁棒性之间的权衡，它们之间呈反相关。为了解决由此产生的折衷，我们引入了一个新的标准，该标准评估了观测值与线性模型之间的一致性。提出的标准确定了唯一的鲁棒性令人满意的回归，并揭示了仅具有弱假设的观测结果的潜在不确定性水平。这些算法被证明可用于线性回归到脑机接口神经解码的挑战性应用。模型一致的鲁棒性令人满意的回归为相似和不同条件下的新观测提供了卓越的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号