• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国政府科技报告 >Robust Lasso with Missing and Grossly Corrupted Observations.
【24h】

Robust Lasso with Missing and Grossly Corrupted Observations.

机译：坚固的套索，缺失和严重损坏的观察。

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper studies the problem of accurately recovering a sparse vector Beta* from highly corrupted linear measurements y = X Beta* + e* + w where e* is a sparse error vector whose nonzero entries may be unbounded and w is a bounded noise. We propose a so-called extended Lasso optimization which takes into consideration sparse prior information of both Beta* and e*. Our first result shows that the extended Lasso can faithfully recover both the regression and the corruption vectors. Our analysis is relied on a notion of extended restricted eigenvalue for the design matrix X. Our second set of results applies to a general class of Gaussian design matrix X with i.i.d rows N(0, Sigma), for which we provide a surprising phenomenon: the extended Lasso can recover exact signed supports of both Beta* and e* from only Omega (k log p log n) observations, even the fraction of corruption is arbitrarily close to one. Our analysis also shows that this amount of observations required to achieve exact signed support is optimal.

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