Sequential Approximation Algorithm for Big Data
Department of Mathematics, The Ohio State University
2017-06-05 ~ 2017-06-05
Science Building No. 1 1493
One of the central tasks in scientific computing is to accurately approximate unknown functions, by using the data-samples of the unknown functions. The recent emergence of big data presents both opportunities and challenges in this field. On one hand, big data introduces more information about the unknowns and, in principle, allows us to create more accurate models. On the other hand, data storage and processing become highly challenging. In this talk, we present a new type of algorithms to create accurate approximation models in very high dimensional spaces using streaming data, without the need to store the entire data set as well as the model matrix. Special attention will be paid on a randomized tensor quadrature method, which is joint work with Prof. Dongbin Xiu and Mr. Yeonjong Shin.