학술논문
Robust GMM Parameter Estimation via the K-BM Algorithm
Document Type
Conference
Author
Source
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Acoustics, Speech and Signal Processing (ICASSP), ICASSP 2023 - 2023 IEEE International Conference on. :1-5 Jun, 2023
Subject
Language
ISSN
2379-190X
Abstract
In this paper, we develop an expectation-maximization (EM)-like scheme, called ${\mathcal{K}}$-BM, for iterative numerical computation of the minimum ${\mathcal{K}}$-divergence estimator (M${\mathcal{K}}$DE). This estimator utilizes Parzen’s non-parameteric ${\mathcal{K}}$ernel density estimate to down weight low density areas attributed to outliers. Similarly to the standard EM algorithm, the ${\mathcal{K}}$-BM involves successive Maximizations of lower Bounds on the objective function of the M${\mathcal{K}}$DE. Differently from EM, these bounds do not rely on conditional expectations only. The proposed ${\mathcal{K}}$-BM algorithm is applied to robust parameter estimation of a finite-order multivariate Gaussian mixture model (GMM). Simulation studies illustrate the performance advantage of the ${\mathcal{K}}$-BM as compared to other state-of-the-art robust GMM estimators.