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  • Research Article
  • 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:


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 Estimation
  • Information Technology
  • Computer Vision
  • Discriminant Analysis
  • Original Image

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Authors’ Affiliations

Department of Computer Engineering, Sharif University of Technology, P.O. Box 11155-8639, Tehran, 1458889694, 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.