SLANTS: Sequential adaptive nonlinear modeling of time series
Q Han, J Ding, EM Airoldi… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
We propose a method for adaptive nonlinear sequential modeling of time series data. Data
are modeled as a nonlinear function of past values corrupted by noise, and the underlying …
are modeled as a nonlinear function of past values corrupted by noise, and the underlying …
Separation of system dynamics and line spectra via sparse representation
L Ning, TT Georgiou… - 49th IEEE Conference on …, 2010 - ieeexplore.ieee.org
The purpose of this work is to explore the use of sparse representation in the context of
system identification and, in particular, to address the important problem of identifying …
system identification and, in particular, to address the important problem of identifying …
Modeling nonlinearity in multi-dimensional dependent data
Q Han, J Ding, E Airoldi… - 2017 IEEE Global …, 2017 - ieeexplore.ieee.org
Given massive data that may be time dependent and multi-dimensional, how to efficiently
explore the underlying functional relationships across different dimensions and time lags? In …
explore the underlying functional relationships across different dimensions and time lags? In …
[图书][B] Fundamental limits and constructive methods for estimation and sensing of sparse signals
B Babadi - 2011 - search.proquest.com
The ever-growing size of everyday data in the digital age has urged researchers to devise
new techniques for measurement, storage and restoration. A promising mathematical theory …
new techniques for measurement, storage and restoration. A promising mathematical theory …
[PDF][PDF] High resolution analysis via sparsity-inducing techniques: spectral lines in colored noise
The impact of sparsity-inducing techniques in signal analysis has been recognized for over
ten years now and has been the key to a growing literature on the subject–commonly …
ten years now and has been the key to a growing literature on the subject–commonly …