Classification on Grassmann Manifold via Scheiddegger-Watson Distribution using Bayesian Approach

Muhammad Ali; Michael Antolovich

doi:10.1109/ISCMI.2016.37

Classification on Grassmann Manifold via Scheiddegger-Watson Distribution using Bayesian Approach

Research output: Book chapter/Published conference paper › Conference paper › peer-review

Abstract

Our focus in this paper is a simple Bayesian classification on generalised Scheiddegger-Watson distribution using standard Maximum Likelihood Estimation (MLE). The main barrier in working with Scheiddegger-Watson or matrix variate distributions via standard MLE is the normalising constant that always appears with them. We apply Taylor expansion for approximating the corresponding matrix-based normalising constant and then implement our proposed approach for classification on Grassmann manifold. We then evaluate the effectiveness of our proposed method on real world data against the state of the art recent techniques and show that the proposed approach outperforms or good comparable with them.

Original language	English
Title of host publication	Proceedings of the 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	28-32
Number of pages	5
ISBN (Electronic)	9781509036967
DOIs	https://doi.org/10.1109/ISCMI.2016.37
Publication status	Published - 02 Oct 2017
Event	3rd International Conference on Soft Computing & Machine Intelligence : ISCMI 2016 - Flora Grand Hotel, Dubai, United Arab Emirates Duration: 23 Nov 2016 → 25 Nov 2016 Conference number: 3rd http://www.iscmi.us/ISCMI2016.html

Conference

Conference	3rd International Conference on Soft Computing & Machine Intelligence
Country/Territory	United Arab Emirates
City	Dubai
Period	23/11/16 → 25/11/16
Internet address	http://www.iscmi.us/ISCMI2016.html

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10.1109/ISCMI.2016.37

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title = "Classification on Grassmann Manifold via Scheiddegger-Watson Distribution using Bayesian Approach",

abstract = "Our focus in this paper is a simple Bayesian classification on generalised Scheiddegger-Watson distribution using standard Maximum Likelihood Estimation (MLE). The main barrier in working with Scheiddegger-Watson or matrix variate distributions via standard MLE is the normalising constant that always appears with them. We apply Taylor expansion for approximating the corresponding matrix-based normalising constant and then implement our proposed approach for classification on Grassmann manifold. We then evaluate the effectiveness of our proposed method on real world data against the state of the art recent techniques and show that the proposed approach outperforms or good comparable with them.",

keywords = "Classification, Grassmann manifolds, Normalising Constant, Scheiddegger-Watson Distribution",

author = "Muhammad Ali and Michael Antolovich",

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doi = "10.1109/ISCMI.2016.37",

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publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

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Ali, M & Antolovich, M 2017, Classification on Grassmann Manifold via Scheiddegger-Watson Distribution using Bayesian Approach. in Proceedings of the 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016., 8057433, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 28-32, 3rd International Conference on Soft Computing & Machine Intelligence , Dubai, United Arab Emirates, 23/11/16. https://doi.org/10.1109/ISCMI.2016.37

Classification on Grassmann Manifold via Scheiddegger-Watson Distribution using Bayesian Approach. / Ali, Muhammad; Antolovich, Michael.
Proceedings of the 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016. United States: IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 28-32 8057433.

Research output: Book chapter/Published conference paper › Conference paper › peer-review

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T1 - Classification on Grassmann Manifold via Scheiddegger-Watson Distribution using Bayesian Approach

AU - Ali, Muhammad

AU - Antolovich, Michael

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N2 - Our focus in this paper is a simple Bayesian classification on generalised Scheiddegger-Watson distribution using standard Maximum Likelihood Estimation (MLE). The main barrier in working with Scheiddegger-Watson or matrix variate distributions via standard MLE is the normalising constant that always appears with them. We apply Taylor expansion for approximating the corresponding matrix-based normalising constant and then implement our proposed approach for classification on Grassmann manifold. We then evaluate the effectiveness of our proposed method on real world data against the state of the art recent techniques and show that the proposed approach outperforms or good comparable with them.

AB - Our focus in this paper is a simple Bayesian classification on generalised Scheiddegger-Watson distribution using standard Maximum Likelihood Estimation (MLE). The main barrier in working with Scheiddegger-Watson or matrix variate distributions via standard MLE is the normalising constant that always appears with them. We apply Taylor expansion for approximating the corresponding matrix-based normalising constant and then implement our proposed approach for classification on Grassmann manifold. We then evaluate the effectiveness of our proposed method on real world data against the state of the art recent techniques and show that the proposed approach outperforms or good comparable with them.

KW - Classification

KW - Grassmann manifolds

KW - Normalising Constant

KW - Scheiddegger-Watson Distribution

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BT - Proceedings of the 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016

PB - IEEE, Institute of Electrical and Electronics Engineers

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Ali M, Antolovich M. Classification on Grassmann Manifold via Scheiddegger-Watson Distribution using Bayesian Approach. In Proceedings of the 2016 3rd International Conference on Soft Computing and Machine Intelligence, ISCMI 2016. United States: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 28-32. 8057433 Epub 2016 Nov 23. doi: 10.1109/ISCMI.2016.37

Classification on Grassmann Manifold via Scheiddegger-Watson Distribution using Bayesian Approach

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