NDLI: Expectation-maximization algorithms for inference in Dirichlet processes mixture

Please wait, while we are loading the content...

Pattern Analysis & Applications : Volume 20

Pattern Analysis & Applications : Volume 19

Pattern Analysis & Applications : Volume 18

Pattern Analysis & Applications : Volume 17

Pattern Analysis & Applications : Volume 16

Pattern Analysis & Applications : Volume 16, Issue 4, November 2013

Pattern Analysis & Applications : Volume 16, Issue 3, August 2013

Pattern Analysis & Applications : Volume 16, Issue 2, May 2013

Pattern Analysis & Applications : Volume 16, Issue 1, February 2013

Supervised texture classification: color space or texture feature selection?

Ant colony clustering analysis based intelligent fault diagnosis method and its application to rotating machinery

Automated classification of touching or overlapping M-FISH chromosomes by region fusion and homolog pairing

Facial emotional classification: from a discrete perspective to a continuous emotional space

Expectation-maximization algorithms for inference in Dirichlet processes mixture

Rotation invariant features for color texture classification and retrieval under varying illumination

A unifying methodology for the evaluation of neural network models on novelty detection tasks

On utilizing dependence-based information to enhance micro-aggregation for secure statistical databases

Transductive multi-distance learning for video search

Pattern Analysis & Applications : Volume 15

Pattern Analysis & Applications : Volume 14

Pattern Analysis & Applications : Volume 13

Pattern Analysis & Applications : Volume 12

Pattern Analysis & Applications : Volume 11

Pattern Analysis & Applications : Volume 10

Pattern Analysis & Applications : Volume 9

Pattern Analysis & Applications : Volume 8

Pattern Analysis & Applications : Volume 7

Pattern Analysis & Applications : Volume 6

Pattern Analysis & Applications : Volume 5

Pattern Analysis & Applications : Volume 4

Pattern Analysis & Applications : Volume 3

Pattern Analysis & Applications : Volume 2

Pattern Analysis & Applications : Volume 1

Expectation-maximization algorithms for inference in Dirichlet processes mixture

Content Provider	Springer Nature Link
Author	Kimura, T. Tokuda, T. Nakada, Y. kajima, T. Matsumoto, T. Doucet, A.
Copyright Year	2011
Abstract	Mixture models are ubiquitous in applied science. In many real-world applications, the number of mixture components needs to be estimated from the data. A popular approach consists of using information criteria to perform model selection. Another approach which has become very popular over the past few years consists of using Dirichlet processes mixture (DPM) models. Both approaches are computationally intensive. The use of information criteria requires computing the maximum likelihood parameter estimates for each candidate model whereas DPM are usually trained using Markov chain Monte Carlo (MCMC) or variational Bayes (VB) methods. We propose here original batch and recursive expectation-maximization algorithms to estimate the parameters of DPM. The performance of our algorithms is demonstrated on several applications including image segmentation and image classification tasks. Our algorithms are computationally much more efficient than MCMC and VB and outperform VB on an example.
Starting Page	55
Ending Page	67
Page Count	13
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	16
Issue Number	1
e-ISSN	1433755X
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-12-06
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Clustering Dirichlet processes Expectation-maximization Finite mixture models Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Expectation-maximization algorithms for inference in Dirichlet processes mixture

TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models

Bayesian mixture of AR models for time series clustering

Unsupervised selection of a finite Dirichlet mixture model: an MML-based approach

CDP Mixture Models for Data Clustering

Clustering spatial data with a hybrid EM approach

Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clustering

Two-View Motion Segmentation with Model Selection and Outlier Removal by RANSAC-Enhanced Dirichlet Process Mixture Models

Robust Factorization Methods Using a Gaussian/Uniform Mixture Model

Expectation-maximization algorithms for inference in Dirichlet processes mixture

Similar Documents

Expectation-maximization algorithms for inference in Dirichlet processes mixture

TRUST-TECH-Based Expectation Maximization for Learning Finite Mixture Models

Bayesian mixture of AR models for time series clustering

Unsupervised selection of a finite Dirichlet mixture model: an MML-based approach

CDP Mixture Models for Data Clustering

Clustering spatial data with a hybrid EM approach

Neural Mixture Models with Expectation-Maximization for End-to-end Deep Clustering

Two-View Motion Segmentation with Model Selection and Outlier Removal by RANSAC-Enhanced Dirichlet Process Mixture Models

Robust Factorization Methods Using a Gaussian/Uniform Mixture Model

Expectation-maximization algorithms for inference in Dirichlet processes mixture