Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2008.2004963
Title: Matrix-variate factor analysis and its applications
Authors: Xie, X.
Yan, S. 
Kwok, J.T.
Huang, T.S.
Keywords: Conditional expectation maximization (EM)
Face recognition
Factor analysis (FA)
Matrix
Issue Date: 2008
Source: Xie, X., Yan, S., Kwok, J.T., Huang, T.S. (2008). Matrix-variate factor analysis and its applications. IEEE Transactions on Neural Networks 19 (10) : 1821-1826. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2008.2004963
Abstract: 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. © 2008 IEEE.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/56589
ISSN: 10459227
DOI: 10.1109/TNN.2008.2004963
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