English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
 Local TagsRelease HistoryDetailsSummary
  Data modeling with the elliptical gamma distribution

Sra, S., Hosseini, R., Theis, L., & Bethge, M. (2015). Data modeling with the elliptical gamma distribution. In G. Lebanon, & S. Vishwanathan (Eds.), Artificial Intelligence and Statistics, 9-12 May 2015, San Diego, California, USA (pp. 903-911). Madison, WI, USA: International Machine Learning Society.

Item is

 

show all hide all

Basic

show hide
Item Permalink: https://hdl.handle.net/11858/00-001M-0000-002A-4671-B Version Permalink: https://hdl.handle.net/21.11116/0000-0001-ACA2-6
Genre: Conference Paper

Files

show Files

Locators

show
hide
Locator:
http://jmlr.org/proceedings/papers/v38/sra15.html (Publisher version)
Description:
-
OA-Status:

Creators

show
hide
 Creators:
Sra, S, Author           
Hosseini, R, Author           
Theis, L1, Author           
Bethge, M1, Author           
Affiliations:
1Werner Reichardt Centre for Integrative Neuroscience, Tübingen, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: We study mixture modeling using the elliptical gamma (EG) distribution, a non-Gaussian distribution that allows heavy and light tail and peak behaviors. We first consider maximum likelihood parameter estimation, a task that turns out to be very challenging: we must handle positive definiteness constraints, and more crucially, we must handle possibly nonconcave log-likelihoods, which makes maximization hard. We overcome these difficulties by developing algorithms based on fixed-point theory; our methods respect the psd constraint, while also efficiently solving the (possibly) nonconcave maximization to global optimality. Subsequently, we focus on mixture modeling using EG distributions: we present a closed-form expression of the KL-divergence between two EG distributions, which we then combine with our ML estimation methods to obtain an efficient split-and-merge expectation maximization algorithm. We illustrate the use of our model and algorithms on a dataset of natural image patches.

Details

show
hide
Language(s):
 Dates: 2015-05
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: SraHTB2015
 Degree: -

Event

show
hide
Title: 18th International Conference on Artificial Intelligence and Statistics (AISTATS 2015)
Place of Event: San Diego, CA, USA
Start-/End Date: -

Legal Case

show

Project information

show

Source 1

show
hide
Title: Artificial Intelligence and Statistics, 9-12 May 2015, San Diego, California, USA
Source Genre: Proceedings
 Creator(s):
Lebanon, G, Editor
Vishwanathan, SVN, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 903 - 911 Identifier: -

Source 2

show
hide
Title: JMLR Workshop and Conference Proceedings
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 38 Sequence Number: - Start / End Page: - Identifier: -