Local Low-Rank Matrix Approximation

Date

May 7, 2014

Overview

Matrix approximation is a common tool in recommendation systems, text mining, and computer vision. A prevalent assumption in constructing matrix approximations is that the partially observed matrix is of low-rank. We propose a new matrix approximation model where we assume instead that the matrix is locally of low-rank, leading to a representation of the observed matrix as a weighted sum of low-rank matrices. We analyze the accuracy of the proposed local low-rank modeling. Our experiments show significant improvements in prediction accuracy in the context of recommendation systems.

Speakers

Guy Lebanon

Guy Lebanon is a senior manager at Amazon, where he leads the Machine Learning Science Group. Prior to that he was a tenured professor at the Georgia Institute of Technology and a scientist at Google and Yahoo. His main research areas are machine learning and data science. Guy received his PhD in 2005 from Carnegie Mellon University and BA, and MS degrees from Technion – Israel Institute of Technology. Dr. Lebanon has authored over 60 refereed publications. He is an action editor of Journal of Machine Learning Research, was the program chair of the 2012 ACM CIKM Conference, and will be the conference co-chair of AI & Statistics (AISTATS 2015). He received the NSF CAREER Award, the ICML best paper runner-up award, the Yahoo Faculty Research and Engagement Award, and is a Siebel Scholar

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