Learning on probabilistic modelling using Grassmann Manifolds

Muhammad Ali, Junbin Gao

Research output: Book chapter/Published conference paperConference paperpeer-review


Probabilistic models are playing an important role as building blocks in more sophisticated geometric parametric models. Although probabilistic or parametric models are seemingly very straightforward for making Bayesian inferences from data that are assumed to be drawn from some model into consideration, the barrier to the Bayesian approach is the normalising constants naturally appearing with them. Our main focus in this paper is to make inferences on matrix variate parametric models via Maximum Likelihood Estimation (MLE) using a simple Bayesian approach. For calculating the value of the matrix based normalising constant we propose the Taylor expansion method for very high concentrated parameter. For inferences we have considered the matrix variate Bingham model on Grassman manifolds. The model is then tested for validity and performance on real World databases with best accuracy estimates.
Original languageEnglish
Title of host publicationProceedings of the 14th International Conference on Frontiers of Information Technology (FIT 2016)
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
Publication statusPublished - 21 Dec 2016
Event14th International Conference on Frontiers of Information Technology: FIT 2016 - Serena Hotel, Islamabad, Pakistan
Duration: 19 Dec 201621 Dec 2016


Conference14th International Conference on Frontiers of Information Technology
Abbreviated titleInternet of Things (IoT)
Internet address

Fingerprint Dive into the research topics of 'Learning on probabilistic modelling using Grassmann Manifolds'. Together they form a unique fingerprint.

Cite this