Estimation of the joint probability of multisensory signals

Hao Pan, Zhi Pei Liang, Thomas S. Huang

Research output: Contribution to journalArticlepeer-review


This paper presents a novel method for estimation of the joint probability of multisensory signals by introducing dimension-reduction mapping functions based on the principle of maximum entropy. A maximum mutual information criterion is derived for selecting the desired mapping functions. An algorithm is further presented for linear transformations of Gaussian random vectors. Experimental results are shown to demonstrate the performance of the proposed method.

Original languageEnglish (US)
Pages (from-to)1431-1437
Number of pages7
JournalPattern Recognition Letters
Issue number13
StatePublished - Nov 2001


  • Canonical correlation analysis
  • Joint probability
  • Maximum mutual information
  • Multisensory information fusion
  • The maximum entropy principle

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering


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