Abstract
This is an era of data deluge with individuals and pervasive sensors acquiring large and ever-increasing amounts of data. Nevertheless, given the inherent redundancy, the costs related to data acquisition, transmission, and storage can be reduced if the per-datum importance is properly exploited. In this context, the present paper investigates sparse linear regression with censored data that appears naturally under diverse data collection setups. A practical censoring rule is proposed here for data reduction purposes. A sparsity-aware censored maximum-likelihood estimator is also developed, which fits well to big data applications. Building on recent advances in online convex optimization, a novel algorithm is finally proposed to enable real-time processing. The online algorithm applies even to the general censoring setup, while its simple closed-form updates enjoy provable convergence. Numerical simulations corroborate its effectiveness in estimating sparse signals from only a subset of exact observations, thus reducing the processing cost in big data applications.
Original language | English (US) |
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Title of host publication | 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 326-330 |
Number of pages | 5 |
ISBN (Electronic) | 9781479970889 |
DOIs | |
State | Published - Feb 5 2014 |
Event | 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 - Atlanta, United States Duration: Dec 3 2014 → Dec 5 2014 |
Publication series
Name | 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 |
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Other
Other | 2014 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2014 |
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Country/Territory | United States |
City | Atlanta |
Period | 12/3/14 → 12/5/14 |
Bibliographical note
Publisher Copyright:© 2014 IEEE.
Keywords
- Compressive sensing
- Data censoring
- MLE
- Online convex optimization