14,118 research outputs found
A new self-organizing neural gas model based on Bregman divergences
In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural
Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its selforganization
capabilities.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec
The Burbea-Rao and Bhattacharyya centroids
We study the centroid with respect to the class of information-theoretic
Burbea-Rao divergences that generalize the celebrated Jensen-Shannon divergence
by measuring the non-negative Jensen difference induced by a strictly convex
and differentiable function. Although those Burbea-Rao divergences are
symmetric by construction, they are not metric since they fail to satisfy the
triangle inequality. We first explain how a particular symmetrization of
Bregman divergences called Jensen-Bregman distances yields exactly those
Burbea-Rao divergences. We then proceed by defining skew Burbea-Rao
divergences, and show that skew Burbea-Rao divergences amount in limit cases to
compute Bregman divergences. We then prove that Burbea-Rao centroids are
unique, and can be arbitrarily finely approximated by a generic iterative
concave-convex optimization algorithm with guaranteed convergence property. In
the second part of the paper, we consider the Bhattacharyya distance that is
commonly used to measure overlapping degree of probability distributions. We
show that Bhattacharyya distances on members of the same statistical
exponential family amount to calculate a Burbea-Rao divergence in disguise.
Thus we get an efficient algorithm for computing the Bhattacharyya centroid of
a set of parametric distributions belonging to the same exponential families,
improving over former specialized methods found in the literature that were
limited to univariate or "diagonal" multivariate Gaussians. To illustrate the
performance of our Bhattacharyya/Burbea-Rao centroid algorithm, we present
experimental performance results for -means and hierarchical clustering
methods of Gaussian mixture models.Comment: 13 page
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