MLE-Based Learning on Grassmann Manifolds

Muhammad Ali, Junbin Gao, Michael Antolovich

Research output: Book chapter/Published conference paperConference paper

Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
ISBN (Electronic)9781509028962
ISBN (Print)9781509028979
DOIs
Publication statusPublished - 22 Dec 2016
Event2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Mantra on View Hotel, Surfer's Paradise, Australia
Duration: 30 Nov 201602 Dec 2016
http://dicta2016.dictaconference.org/

Conference

Conference2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
CountryAustralia
CitySurfer's Paradise
Period30/11/1602/12/16
Internet address

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