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DTSTAMP;TZID=America/Vancouver:20221209T110000
DTSTART;TZID=America/Vancouver:20221209T110000
DTEND;TZID=America/Vancouver:20221209T112500

UID:20221209T110000@prima2022.primamath.org
SUMMARY:Robust Recovery of Low-rank Matrices and Tensors from Noisy Sketches
DESCRIPTION:A common approach for compressing large-scale data is through matrix sketching. In this talk, we consider the problem of recovering low-rank matrices or tensors from noisy sketches. We provide theoretical guarantees characterizing the error between the output of the sketching algorithm and the ground truth low-rank matrix or tensor. Applications of this approach to synthetic data and medical imaging data will be presented.
STATUS:CONFIRMED
LOCATION:Junior Ballroom C
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