Abstract
Data reduction for large-scale linear regression is one of the most important tasks in this era of data deluge. Exact model information is however not often available for big data analytics. Therefore, we propose a framework for big data sketching (i.e., a data reduction tool) that is robust to possible model mismatch. Such a sketching task is cast as a Boolean min-max optimization problem, and then equivalently reduced to a Boolean minimization program. Capitalizing on the block coordinate descent algorithm, a scalable solver is developed to yield an efficient sampler and a good estimate of the unknown regression coefficient.
Original language | English (US) |
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Title of host publication | Conference Record of the 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
Editors | Michael B. Matthews |
Publisher | IEEE Computer Society |
Pages | 97-101 |
Number of pages | 5 |
ISBN (Electronic) | 9781467385763 |
DOIs | |
State | Published - Feb 26 2016 |
Event | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 8 2015 → Nov 11 2015 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Volume | 2016-February |
ISSN (Print) | 1058-6393 |
Other
Other | 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 11/8/15 → 11/11/15 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
Keywords
- Big data
- data reduction
- linear regression
- model mismatch
- sketching