Random-Sampling Based Techniques for Approximated Matrix Multiplication
Date:
One of the most common, but at the same time expensive operations in linear algebra, is multiplying two matrices. With the rapid development of machine learning and increases in data volume, performing fast matrix intensive multiplications has become a major challenge. Two different approaches to overcome this issue are 1) to approximate the product and 2) to perform the multiplication distributively. In this talk, I focuse on the first approach and summarize some random-sampling based techniques for the approximation of the matrix-matrix multiplication, such as the work by Mahoney et al., in SIAM J. Comput’ 2006. [Link] [Slide]