Spectral Methods Meets Asymmetry: Two Recent Stories - Lecture 4

Explore the intersection of asymmetry and spectral methods in this lecture from the TRIAD Distinguished Lecture Series, delivered by Yuxin Chen from Princeton University. Delve into two compelling stories that showcase the effectiveness of eigen-decomposition in handling asymmetrically perturbed low-rank data matrices. Discover how spectral methods enable unparalleled accuracy in top-K ranking from pairwise comparisons, and learn about their superior performance in matrix de-noising and spectral estimation compared to traditional SVD-based approaches. Gain insights into the adaptive nature of these methods in dealing with heteroscedasticity without requiring careful bias correction. Understand the collaborative efforts behind this research, involving work with Cong Ma, Kaizheng Wang, Jianqing Fan, and Chen Cheng.