AI Chat AI Image Generator AI Video Text to Speech

Geometric Models with Co-occurrence Groups

01/30/2011

∙

by Joan Bruna, et al.

∙

∙

A geometric model of sparse signal representations is introduced for classes of signals. It is computed by optimizing co-occurrence groups with a maximum likelihood estimate calculated with a Bernoulli mixture model. Applications to face image compression and MNIST digit classification illustrate the applicability of this model.

Joan Bruna
96 publications
Stéphane Mallat
38 publications

research

∙ 11/08/2020

Consistency of the MLE under a two-parameter gamma mixture model with a structural shape parameter

The finite Gamma mixture model is often used to describe randomness in i...

0 Mingxing He, et al. ∙

research

∙ 08/26/2022

Gaussian likelihood geometry of projective varieties

We explore the maximum likelihood degree of a homogeneous polynomial F o...

0 Sandra Di Rocco, et al. ∙

research

∙ 08/18/2016

A Tight Convex Upper Bound on the Likelihood of a Finite Mixture

The likelihood function of a finite mixture model is a non-convex functi...

0 Elad Mezuman, et al. ∙

research

∙ 03/17/2018

Signal detection via Phi-divergences for general mixtures

In this paper we are interested in testing whether there are any signals...

0 Marc Ditzhaus, et al. ∙

research

∙ 10/17/2014

Inference and Mixture Modeling with the Elliptical Gamma Distribution

We study modeling and inference with the Elliptical Gamma Distribution (...

0 Reshad Hosseini, et al. ∙

research

∙ 08/04/2023

Network Inference Using the Hub Model and Variants

Statistical network analysis primarily focuses on inferring the paramete...

0 Zhibing He, et al. ∙