MLE-Based Learning on Grassmann Manifolds

Ali, M; Gao, J; Antolovich, M

MLE-Based Learning on Grassmann Manifolds

Ali, M Gao, J Antolovich, M

Permalink

Publication Type:: Conference Proceeding
Citation:: 2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016, 2016
Issue Date:: 2016-12-22

Closed Access

	Filename	Description	Size
	07797063.pdf	Published version	825.77 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Ali, M	en_US
dc.contributor.author	Gao, J	en_US
dc.contributor.author	Antolovich, M https://orcid.org/0000-0003-2601-8332	en_US
dc.date.issued	2016-12-22	en_US
dc.identifier.citation	2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016, 2016	en_US
dc.identifier.isbn	9781509028962	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117700
dc.description.abstract	© 2016 IEEE. In this paper we focus on Maximum Likelihood Estimation (MLE) technique for classification on Grassmann manifolds using matrix variate Bingham density function. Unlike the conventional techniques for multivariate distributions in the existing literature e.g., Markov chain Monte Carlo (MCMC) sampling methods, non-parametric methods, Expectation Maximisation (EM) iterative methods or exact methods, we demonstrate a new way of parametric modelling for classification that is strictly based on normalising constant. The evaluation of normalising constant is based on the matrix-variate Saddle Point Approximation (SPA). The Maximum Likelihood Estimation (MLE) is directly employed on the proposed manifold based Bingham density function via simple Bayesian classifier. For numerical experiments a 3-class classification example is considered by using real world Caltech 101 and DynTex++ database. We have compared our average classification accuracy rate with the baseline results taken from the existing state of the art techniques, and found that our method outperforms or at least best comparable.	en_US
dc.relation.ispartof	2016 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2016	en_US
dc.relation.isbasedon	10.1109/DICTA.2016.7797063	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	MLE-Based Learning on Grassmann Manifolds	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US

Abstract:

© 2016 IEEE. In this paper we focus on Maximum Likelihood Estimation (MLE) technique for classification on Grassmann manifolds using matrix variate Bingham density function. Unlike the conventional techniques for multivariate distributions in the existing literature e.g., Markov chain Monte Carlo (MCMC) sampling methods, non-parametric methods, Expectation Maximisation (EM) iterative methods or exact methods, we demonstrate a new way of parametric modelling for classification that is strictly based on normalising constant. The evaluation of normalising constant is based on the matrix-variate Saddle Point Approximation (SPA). The Maximum Likelihood Estimation (MLE) is directly employed on the proposed manifold based Bingham density function via simple Bayesian classifier. For numerical experiments a 3-class classification example is considered by using real world Caltech 101 and DynTex++ database. We have compared our average classification accuracy rate with the baseline results taken from the existing state of the art techniques, and found that our method outperforms or at least best comparable.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/117700