Recognition on Matrix Angular Central Gaussian Distribution

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


We demonstrate the standard approach of Maximum Likelihood Estimation (MLE) for practicability of Grassmann Angular Central Gaussian (GACG) distribution by using Grassmann manifold. Our main concern is then on the applicability of GACG for computer vision application e.g., classification on arbitrarily high dimensional Grassmannian space. We show by numerical experiments that the implementation of the proposed Grassmannian variate parametric model via MLE using simple Bayesian classifier is directly related to the accurate calculation of normalising constant naturally appearing with them. We verify the validity and performance of our proposed approach on two publicly available databases against the existing state of art techniques, where we observed that the classification accuracy of our proposed approach outperforms significantly.
Original languageEnglish
Title of host publicationProceedings of the 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781509036967
Publication statusPublished - 02 Oct 2017
Event3rd International Conference on Soft Computing & Machine Intelligence : ISCMI 2016 - Flora Grand Hotel, Dubai, United Arab Emirates
Duration: 23 Nov 201625 Nov 2016
Conference number: 3rd


Conference3rd International Conference on Soft Computing & Machine Intelligence
Country/TerritoryUnited Arab Emirates
Internet address


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