TY - GEN
T1 - Online censoring for large-scale regressions
AU - Berberidis, D.
AU - Wang, G.
AU - Giannakis, G. B.
AU - Kekatos, V.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2015/4/24
Y1 - 2015/4/24
N2 - 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.
AB - 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.
KW - D.4. Adaptive Filtering
KW - Technical Area
UR - https://www.scopus.com/pages/publications/84940483059
U2 - 10.1109/ACSSC.2014.7094386
DO - 10.1109/ACSSC.2014.7094386
M3 - Conference contribution
AN - SCOPUS:84940483059
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 14
EP - 18
BT - Conference Record of the 48th Asilomar Conference on Signals, Systems and Computers
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 48th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Y2 - 2 November 2014 through 5 November 2014
ER -