Getting more with less: matrix and tensor algorithms from subsampling modes | Do (N)ASK seminar скачать в хорошем качестве

Getting more with less: matrix and tensor algorithms from subsampling modes | Do (N)ASK seminar

#DoNASK #Seminar #DataScience #algorithm ✅ Do you know that you can factorize a matrix into a product of its submatrices? ✅ How can we use this to build algorithms for data analysis? ✅ Can we build algorithms to denoise or complete matrices by only viewing small parts of them? During the Do (N)ASK seminar, we will overview some matrix factorizations that allow one to observe only small randomly chosen submatrices of a data matrix, and how these factorizations can be applied in the design of fast algorithms for certain tasks such as Robust PCA or matrix completion. We show how to obtain state-of-the-art runtime for these tasks and apply the algorithms to some image and video processing tasks. We will discuss some natural generalizations of this approach to tensor data. ➡️ Keaton Hamm has been an Assistant Professor of Mathematics at the ‪@UTArlington‬ since 2020. He previously held postdoc positions at Vanderbilt University and the University of Arizona, and obtained his Ph.D. from Texas A&M University in 2015. His current research interests are in computational and randomized linear (and multilinear) algebra with applications to computer vision and image processing, and nonlinear dimensionality reduction and optimal transport with applications to imaging. His research has been funded by the National Science Foundation, the Army Research Office, and the U.S. Department of Agriculture.