Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Monotonic alpha-divergence minimisation

Abstract : In this paper, we introduce a novel iterative algorithm which carries out $\alpha$-divergence minimisation by ensuring a systematic decrease in the $\alpha$-divergence at each step. In its most general form, our framework allows us to simultaneously optimise the weights and components parameters of a given mixture model. Notably, our approach permits to build on various methods previously proposed for $\alpha$-divergence minimisation such as gradient or power descent schemes. Furthermore, we shed a new light on an integrated Expectation Maximization algorithm. We provide empirical evidence that our methodology yields improved results, all the while illustrating the numerical benefits of having introduced some flexibility through the parameter $\alpha$ of the $\alpha$-divergence.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

https://hal.telecom-paris.fr/hal-03164338
Contributor : Kamélia Daudel <>
Submitted on : Tuesday, March 9, 2021 - 8:55:47 PM
Last modification on : Wednesday, March 31, 2021 - 10:23:41 AM

File

ddr_2021_hal.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-03164338, version 1

Citation

Kamélia Daudel, Randal Douc, François Roueff. Monotonic alpha-divergence minimisation. 2021. ⟨hal-03164338⟩

Share

Metrics

Record views

55

Files downloads

9