On sunday is the 2010 Int. Symp. on Voronoi Diagrams (ISVD). We shall present: Jensen-Bregman Voronoi diagrams and centroidal tessellations Here are the slides of the talk. The Jensen-Bregman divergence is a distortion measure defined by the Jensen difference provided by a strictly

The following work has been presented at CVPR 2010. It considers orthogonal projection onto a tangent plane instead of vertical projection when defining a distance using a convex generator. Shape database search is ubiquitous in the world of biometric systems,

Simplification and hierarchical representations of mixtures of exponential families just got out at Signal Processing Abstract A mixture model in statistics is a powerful framework commonly used to estimate the probability measure function of a random variable. Most algorithms handling mixture models

Yet another report by Olivier who attended RFIA 2010 RFIA 2010 — Reconnaissance de formes et intelligence artificielle RFIA (which stands for Reconnaissance de formes et intelligence artificielle) is a French-speaking conference about pattern recognition and artificial intelligence. The 17th

Olivier attended the VISAPP conference where he presented results on Reranking with Contextual dissimilarity measures from representational Bregman k-means Here is the abstract followed by a short report of the conference. We present a novel reranking framework for Content Based

The Jensen-Bregman divergence is a distortion measure defined by the Jensen difference provided by a strictly convex function. Jensen-Bregman divergences extend the well-known Jensen-Shannon divergence by allowing to choose an arbitrary convex function generator instead of the standard Shannon entropy.

We study the centroid with respect to the class of information-theoretic distortion measures called Burbea-Rao divergences. Burbea-Rao divergences generalize the Jensen-Shannon divergence by measuring the non-negative Jensen difference induced by a strictly convex and differentiable function expressing a measure of

Well, the title of the blog is computational information geometry. It includes the word “computational”. Of course, one aspects is to bring computational geometry vistas to information geometry by fostering algorithmic techniques. Another aspect, is to ponder what can be

I recently read articles on paleomagnetism. It is common to make the assumption of antipodal symmetry for the distribution of the dispersion of the directions. Two such spherical distributions (directional statistics) with such an antipodal symmetry are the Bingham and

Olivier was kind enough to provide this report trip on the French computational geometry days, as known as JGA 2010. The plenary speaker slides are online and worth a check. The Journées de Géométrie Algorithmique are the main French-speaking meeting about computational

Shannon entropy is said additive in the sense that the entropy of the joint distribution H(X*Y) is the sum of the entropies: H(X*Y) = H(X)+H(Y). This property is not true for the quadratic entropy (sum of squares). The Java program

A long time ago, well in 1996, I investigated output-sensitive algorithms. I then designed an algorithm for peeling iteratively the convex hulls of a 2D point set. This yields the notion of depth of a point set, and is a

The center of mass (=centroid) is defined as the center point minimizing the squared of the Euclidean distances (=variance). If one of the source point is an outlier corrupting your dataset, and if that outlier goes to infinity, then your

They are only 6 exponential family distributions that admit variance as a quadratic function (QVF=quadratic variance function) of the parameter. For the multivariate case, it is a bit more complex but well defined and studied: The $2d+4$ simple quadratic natural