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|Title:||Matrix-variate factor analysis and its applications|
|Keywords:||Conditional expectation maximization (EM)|
Factor analysis (FA)
|Citation:||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|
|Appears in Collections:||Staff Publications|
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