Weighted-data GMM @ IEEE TPAMI

Israel-Dejene Gebru, Xavier Alameda-Pineda, Florence Forbes and Radu Horaud

We recently got a paper accepted at TPAMI!

We had this crazy idea of mixture models that are trained with weighted-data. Imagine you want to train a GMM, and each data point comes with a weight denoting the relevance of the data point for the model.We also explored the case in which this external relevance information is taken into account under the form of a prior. This is useful in cases where the external information is inaccurate.

You are more than welcome to take a look at the preprint [1].

References

1. I. Gebru, X. Alameda-Pineda, F. Forbes, and R. Horaud, “EM algorithms for weighted-data clustering with application to audio-visual scene analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, iss. 12, pp. 2402-2415, 2016. [ bib pdf code data arxiv ]
   

   @article{Gebru-TPAMI-2016,
      title = {{EM} algorithms for weighted-data clustering with application to audio-visual scene analysis},
      author = {Israel-Dejene Gebru and Xavier Alameda-Pineda and Florence Forbes and Radu Horaud},
      journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
      year={2016},
      volume={38},
      number={12},
      pages={2402-2415},
      url = {http://arxiv.org/abs/1509.01509},
      arxiv = {http://arxiv.org/abs/1509.01509},
      doi={10.1109/TPAMI.2016.2522425},
      soft={http://perception.inrialpes.fr/people/Gebru/code/WD-EM.zip},
      data={https://team.inria.fr/perception/avtrack1/},
     pdf={http://xavirema.eu/wp-content/papercite-data/pdf/Gebru-TPAMI-2016.pdf}
   }