Abstract
As every day 2.5 quintillion bytes of data are generated, the era of Big Data is undoubtedly upon us. Nonetheless, a significant percentage of the data accrued can be omitted while maintaining a certain quality of statistical inference with a limited computational budget. In this context, estimating adaptively high-dimensional signals from massive data observed sequentially is challenging but equally important in practice. The present paper deals with this challenge based on a novel approach that leverages interval censoring for data reduction. An online maximum likelihood, least mean-square (LMS)-type algorithm, and an online support vector regression algorithm are developed for censored data. The proposed algorithms entail simple, low-complexity, closed-form updates, and have provably bounded regret. Simulated tests corroborate their efficacy.
Original language | English (US) |
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Title of host publication | Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers |
Editors | Michael B. Matthews |
Publisher | IEEE Computer Society |
Pages | 14-18 |
Number of pages | 5 |
ISBN (Electronic) | 9781479982974 |
DOIs | |
State | Published - Apr 24 2015 |
Event | 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 2 2014 → Nov 5 2014 |
Publication series
Name | Conference Record - Asilomar Conference on Signals, Systems and Computers |
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Volume | 2015-April |
ISSN (Print) | 1058-6393 |
Other
Other | 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 |
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Country/Territory | United States |
City | Pacific Grove |
Period | 11/2/14 → 11/5/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- D.4. Adaptive Filtering
- Technical Area