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Activity Number: 172 - Machine Learning and Algorithms
Type: Contributed
Date/Time: Monday, July 31, 2017 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #323096 View Presentation
Title: Streaming Matrix Completion with Automated Rank Selection
Author(s): Milo Page* and Christopher M Gotwalt and Alyson Wilson
Companies: JMP/NCSU and JMP and North Carolina State University
Keywords: Matrix Completion ; Missing Data ; Streaming Algorithm ; Computational Statistics
Abstract:

Matrix completion, a process of using a low rank approximation to impute missing values in a data matrix, offers a general and powerful framework for imputation. It has received significant attention over the last decade and a plethora of new methods for faster, more accurate convergence have been developed. Two main limitations remain restricting its implementation in statistical practice - choosing an appropriate rank and scoring new observations without needing to recalculate the entire model (i.e. performing streaming matrix completion). These limitations result in matrix completion remaining a relatively esoteric imputation method, when often the generality of a low rank approximation fits a variety of data well. We suggest a way to address both problems, which allows for a fully automated streaming matrix completion algorithm. While fine tuning options are available, this algorithm requires at a minimum only a single click from the user to fit a streaming imputation model with error estimates generated by cross-validation


Authors who are presenting talks have a * after their name.

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