skip to main content

Search for: All records

Creators/Authors contains: "Li, Yuanxin"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. null (Ed.)
  2. null (Ed.)
  3. null (Ed.)
  4. null (Ed.)
  5. null (Ed.)
  6. Recent work has demonstrated the effectiveness of gradient descent for recovering low-rank matrices from random linear measurements in a globally convergent manner. However, their performance is highly sensitive in the presence of outliers that may take arbitrary values, which is common in practice. In this paper, we propose a truncated gradient descent algorithm to improve the robustness against outliers, where the truncation is performed to rule out the contributions from samples that deviate significantly from the sample median. A restricted isometry property regarding the sample median is introduced to provide a theoretical footing of the proposed algorithm for the Gaussian orthogonal ensemble. Extensive numerical experiments are provided to validate the superior performance of the proposed algorithm. 
    more » « less