TY - GEN
T1 - Quickest convergence of online algorithms via data selection
AU - Romero, Daniel
AU - Berberidis, Dimitris
AU - Giannakis, Georgios B
PY - 2016/5/18
Y1 - 2016/5/18
N2 - Big data applications demand efficient solvers capable of providing accurate solutions to large-scale problems at affordable computational costs. Processing data sequentially, online algorithms offer attractive means to deal with massive data sets. However, they may incur prohibitive complexity in high-dimensional scenarios if the entire data set is processed. It is therefore necessary to confine computations to an informative subset. While existing approaches have focused on selecting a prescribed fraction of the available data vectors, the present paper capitalizes on this degree of freedom to accelerate the convergence of a generic class of online algorithms in terms of processing time/computational resources by balancing the required burden with a metric of how informative each datum is. The proposed method is illustrated in a linear regression setting, and simulations corroborate the superior convergence rate of the recursive least-squares algorithm when the novel data selection is effected.
AB - Big data applications demand efficient solvers capable of providing accurate solutions to large-scale problems at affordable computational costs. Processing data sequentially, online algorithms offer attractive means to deal with massive data sets. However, they may incur prohibitive complexity in high-dimensional scenarios if the entire data set is processed. It is therefore necessary to confine computations to an informative subset. While existing approaches have focused on selecting a prescribed fraction of the available data vectors, the present paper capitalizes on this degree of freedom to accelerate the convergence of a generic class of online algorithms in terms of processing time/computational resources by balancing the required burden with a metric of how informative each datum is. The proposed method is illustrated in a linear regression setting, and simulations corroborate the superior convergence rate of the recursive least-squares algorithm when the novel data selection is effected.
KW - Big data
KW - adaptive data selection
KW - online learning
KW - stochastic approximation
UR - http://www.scopus.com/inward/record.url?scp=84973390100&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84973390100&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2016.7472866
DO - 10.1109/ICASSP.2016.7472866
M3 - Conference contribution
AN - SCOPUS:84973390100
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 6185
EP - 6189
BT - 2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
Y2 - 20 March 2016 through 25 March 2016
ER -