318 Hanes Hall, CB #3260 Chapel Hill, NC 27599-3260
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STOR Colloquium: Anna Little, Michigan State University

January 8, 2020 @ 3:30 pm - 4:30 pm

Robust Statistical Procedures for Noisy,

High-dimensional Data


This talk addresses two topics related to robust statistical procedures for analyzing noisy, high-dimensional data: (I) path-based spectral clustering and (II) robust multi-reference alignment. Both methods must overcome a large ambient dimension and lots of noise to extract the relevant low dimensional data structure in a computationally efficient way. In (I), the goal is to partition the data into meaningful groups, and this is achieved by a novel approach which combines a data driven metric with graph-based clustering. Using a data driven metric allows for strong theoretical guarantees and fast algorithms when clusters concentrate around low-dimensional sets. In (II), the goal is to recover a hidden signal from many noisy observations of the hidden signal, where each noisy observation includes a random translation, a random dilation, and high additive noise. A wavelet based approach is used to apply a data-driven, nonlinear unbiasing procedure, so that the estimate of the hidden signal is robust to high frequency perturbations.


Refreshments will be served at 3:00pm in the 3rd floor lounge of Hanes Hall



January 8, 2020
3:30 pm - 4:30 pm
Event Category:


120 Hanes Hall
Hanes Hall
Chapel Hill, NC 27599 United States