A regression model with a hidden logistic process for signal parametrization

TL;DR

This paper introduces a novel regression model with a hidden logistic process for signal parametrization, employing EM and IRLS algorithms for parameter estimation, demonstrating effective results on simulated and real data.

Contribution

It presents a new signal parametrization method using a regression model with a hidden logistic process, estimated via EM and IRLS algorithms.

Findings

Good performance on simulated data

Effective on real data

Robust parameter estimation

Abstract

A new approach for signal parametrization, which consists of a specific regression model incorporating a discrete hidden logistic process, is proposed. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The parameters of the hidden logistic process, in the inner loop of the EM algorithm, are estimated using a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm. An experimental study using simulated and real data reveals good performances of the proposed approach.