Robust GMM parameter estimation via the K-BM algorithm

dc.contributor.authorKenig, Ori
dc.contributor.authorTodros, Koby
dc.contributor.authorAdali, Tulay
dc.date.accessioned2023-07-07T16:48:28Z
dc.date.available2023-07-07T16:48:28Z
dc.date.issued2023-05-05
dc.description2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 04-10 June 2023en_US
dc.description.abstractIn 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 K-BM 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.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/10094602en_US
dc.format.extent5 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.genrepostprintsen_US
dc.identifierdoi:10.13016/m2337i-i7iz
dc.identifier.citationO. Kenig, K. Todros and T. Adali, "Robust GMM Parameter Estimation via the K-BM Algorithm," ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023, pp. 1-5, doi: 10.1109/ICASSP49357.2023.10094602.en_US
dc.identifier.urihttps://doi.org/10.1109/ICASSP49357.2023.10094602
dc.identifier.urihttp://hdl.handle.net/11603/28510
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rights© 2023 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.titleRobust GMM parameter estimation via the K-BM algorithmen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0003-0594-2796en_US

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