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Open Access

Matrix-Variate Probabilistic Model for Canonical Correlation Analysis

EURASIP Journal on Advances in Signal Processing20112011:748430

Received: 29 October 2010

Accepted: 31 January 2011

Published: 24 February 2011


Motivated by the fact that in computer vision data samples are matrices, in this paper, we propose a matrix-variate probabilistic model for canonical correlation analysis (CCA). Unlike probabilistic CCA which converts the image samples into the vectors, our method uses the original image matrices for data representation. We show that the maximum likelihood parameter estimation of the model leads to the two-dimensional canonical correlation directions. This model helps for better understanding of two-dimensional Canonical Correlation Analysis (2DCCA), and for further extending the method into more complex probabilistic model. In addition, we show that two-dimensional Linear Discriminant Analysis (2DLDA) can be obtained as a special case of 2DCCA.


Parameter EstimationInformation TechnologyComputer VisionDiscriminant AnalysisOriginal Image

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Authors’ Affiliations

Department of Computer Engineering, Sharif University of Technology, Tehran, Iran


© M. Safayani and M. T. Manzuri Shalmani. 2011

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.