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Authors: Nicola Greggio and Alexandre Bernardino

Affiliation: Instituto de Sistemas e Robótica, Instituto Superior Técnico, 1049-001 Lisbon, Portugal

Keyword(s): Unsupervised Clustering, Gaussian Mixture Models, Greedy Methods, Ill-Posed Problem, Splitting Components.

Abstract: In this work we address the problem of Gaussian Mixture Model estimation with model selection through coarse-to-fine component splitting. We describe a split rule, denoted Principal Direction 2-Gaussian Fit, that projects mixture components onto 1D subspaces and fits a two-component model to the projected data. Good split rules are important for coarse-to-fine Gaussian Mixture Estimation algorithms, that start from a single component covering the whole data and proceed with successive phases of component splitting followed by EM steps until a model selection criteria is optimized. These algorithms are typically faster than alternatives but depend critically in the component splitting method. The advantage of our approach with respect to other split rules is twofold: (1) it has a smaller number of parameters and (2) it is optimal in 1D projections of the data. Because our split rule provides a good initialization for the EM steps, it promotes faster convergence to a solution. We illus trate the validity of this algorithm through a series of experiments, showing a better robustness to the choice of parameters this approach to be faster that state-of-the-art alternatives, while being competitive in terms of data fit metrics and processing time. (More)

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Paper citation in several formats:
Greggio, N. and Bernardino, A. (2025). Principal Direction 2-Gaussian Fit. In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-730-6; ISSN 2184-4313, SciTePress, pages 309-317. DOI: 10.5220/0013250400003905

@conference{icpram25,
author={Nicola Greggio and Alexandre Bernardino},
title={Principal Direction 2-Gaussian Fit},
booktitle={Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2025},
pages={309-317},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013250400003905},
isbn={978-989-758-730-6},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Principal Direction 2-Gaussian Fit
SN - 978-989-758-730-6
IS - 2184-4313
AU - Greggio, N.
AU - Bernardino, A.
PY - 2025
SP - 309
EP - 317
DO - 10.5220/0013250400003905
PB - SciTePress

- Science and Technology Publications, Lda.
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