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Robust Estimation of Gaussian Mixtures from Noisy Input Data

Shaobo Hou, Aphrodite Galata
CVPR 2008
doi:10.1109/CVPR.2008.4587467 /cvpr/2008/hou2008cvpr-robust/

Abstract

We propose a variational bayes approach to the problem of robust estimation of gaussian mixtures from noisy input data. The proposed algorithm explicitly takes into account the uncertainty associated with each data point, makes no assumptions about the structure of the covariance matrices and is able to automatically determine the number of the gaussian mixture components. Through the use of both synthetic and real world data examples, we show that by incorporating uncertainty information into the clustering algorithm, we get better results at recovering the true distribution of the training data compared to other variational bayesian clustering algorithms. ©2008 IEEE.

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Text

Hou and Galata. "Robust Estimation of Gaussian Mixtures from Noisy Input Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008. doi:10.1109/CVPR.2008.4587467

Markdown

[Hou and Galata. "Robust Estimation of Gaussian Mixtures from Noisy Input Data." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2008.](https://mlanthology.org/cvpr/2008/hou2008cvpr-robust/) doi:10.1109/CVPR.2008.4587467

BibTeX

@inproceedings{hou2008cvpr-robust,
  title     = {{Robust Estimation of Gaussian Mixtures from Noisy Input Data}},
  author    = {Hou, Shaobo and Galata, Aphrodite},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {2008},
  doi       = {10.1109/CVPR.2008.4587467},
  url       = {https://mlanthology.org/cvpr/2008/hou2008cvpr-robust/}
}