Title :
Online censoring for large-scale regressions
Author :
Berberidis, D. ; Wang, G. ; Giannakis, G.B. ; Kekatos, V.
Author_Institution :
Dept. of ECE & Digital Tech. Center, Univ. of Minnesota, Minneapolis, MN, USA
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.
Keywords :
Big Data; data reduction; least mean squares methods; maximum likelihood estimation; regression analysis; statistical analysis; support vector machines; Big Data; LMS algorithm; censored data; data reduction; large-scale regression; online censoring; online maximum likelihood least mean-square-type algorithm; online support vector regression algorithm; signal estimation; statistical inference; Big data; Least squares approximations; Linear regression; Maximum likelihood estimation; Support vector machines; Wireless sensor networks; D.4. Adaptive Filtering; Technical Area;
Conference_Titel :
Signals, Systems and Computers, 2014 48th Asilomar Conference on
Print_ISBN :
978-1-4799-8295-0
DOI :
10.1109/ACSSC.2014.7094386
