'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
A sparse representation, with satisfactory approximation accuracy,
is usually desirable in any nonlinear system identification and signal
processing problem. A new forward orthogonal regression algorithm, with
mutual information interference, is proposed for sparse model selection and
parameter estimation. The new algorithm can be used to construct parsimonious
linear-in-the-parameters models