Mark Crovella and Evimaria Terzi awarded NSF grant
Professors Mark Crovella (PI) and Evimaria Terzi (co-PI) of the Computer Science Department received a National Science Foundation award entitled “Structural Matrix Completion for Data Mining Applications.” Congratulations to Mark and Evimaria!
Abstract
A common problem arising in science and engineering is that a dataset may only be partially measured. Often the complete dataset is naturally expressed as a matrix – for example, traffic flows in a city, gene expression across a set of treatments, or ratings of movies for users. The matrix completion problem seeks to infer the missing entries of a matrix, under a low-rank assumption. To date, most matrix completion methods do not actually check whether the known entries contain sufficient information to complete the matrix. Recently, however, a new and very different class of “structural” methods have emerged, which analyze the information content of the visible matrix entries, and so can determine whether accurate completion is possible. From a data mining standpoint, the implications of structural matrix completion methods are largely unexplored. This project will investigate how to leverage structural matrix completion methods to attack a host of data analysis problems, including developing new methods for active matrix completion, new approaches to cross-validating matrix completion results, and new strategies for general matrix completion.