Matrix-variate factor analysis and its applications

Xianchao Xie, Shuicheng Yan, James T. Kwok, Thomas S. Huang

Research output: Contribution to journalArticlepeer-review


Factor analysis (FA) seeks to reveal the relationship between an observed vector variable and a latent variable of reduced dimension. It has been widely used in many applications involving high-dimensional data, such as image representation and face recognition. An intrinsic limitation of FA lies in its potentially poor performance when the data dimension is high, a problem known as curse of dimensionality. Motivated by the fact that images are inherently matrices, we develop, in this brief, an FA model for matrix-variate variables and present an efficient parameter estimation algorithm. Experiments on both toy and real-world image data demonstrate that the proposed matrix-variant FA model is more efficient and accurate than the classical FA approach, especially when the observed variable is high-dimensional and the samples available are limited.

Original languageEnglish (US)
Pages (from-to)1821-1826
Number of pages6
JournalIEEE Transactions on Neural Networks
Issue number10
StatePublished - 2008
Externally publishedYes


  • Conditional expectation maximization (EM)
  • Face recognition
  • Factor analysis (FA)
  • Matrix

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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