@inproceedings{93a0052645f94eddb6247026bf15991e,
title = "Robust GMM Parameter Estimation via the K-BM Algorithm",
abstract = "In this paper, we develop an expectation-maximization (EM)-like scheme, called K-BM, for iterative numerical computation of the minimum K-divergence estimator (MKDE). This estimator utilizes Parzen's non-parameteric Kernel density estimate to down weight low density areas attributed to outliers. Similarly to the standard EM algorithm, the KBM involves successive Maximizations of lower Bounds on the objective function of the MKDE. Differently from EM, these bounds do not rely on conditional expectations only. The proposed 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 K-BM as compared to other state-of-the-art robust GMM estimators.",
keywords = "Divergences, estimation theory, robust statistics",
author = "Ori Kenig and Koby Todros and T{\"u}lay Adali",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.1109/ICASSP49357.2023.10094602",
language = "English",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers",
booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings",
address = "United States",
}