Speaker
Description
Recently, there have been many advances in the area of randomized and sampling-based methods for data approximation. This has led to significant progress towards the efficient treatment of large data in both compression and utilization in computation. In this talk, I will discuss a current work that uses random oversampling on a Tensor Train Cross (TT-Cross) approximation in order to reduce the observed error of a tensor approximation. This work includes two separate formulations utilizing projection techniques to construct tensor cores with results demonstrating a reduction in error. Along with the observed reduction in error, we will show that in practice, the oversampling procedure does
not substantially increase computation time compared to a standard TT-Cross construction.