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Thesis Defense
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Ruhui Jin, PMA 12.166: Tensor dimension reduction and applications
Wednesday, June 29, 2022, 09:00am - 10:00am
Motivated by practical data applications, we study how to efficiently reduce the size of tensor-structured data while preserving the essential information. In this talk, we generalize classical reduction methods for vectors, matrices, such as random projections and low-rank decompositions, to be suitable for higher-order tensors. Numerical experiments show the proposed reduction algorithms result in significant storage savings and computation speed-ups. As a trade-off, from the theoretical perspective, the approximation errors between the original and reduced data are rigorously analyzed. Zoom link: https://utexas.zoom.us/j/92503328148
Location: PMA 12.166

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