Geodesic
An alternating minimization algorithm for Factor Analysis
Kybernetika, Tome 55 (2019) no. 4, pp. 740-754 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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The problem of decomposing a given covariance matrix as the sum of a positive semi-definite matrix of given rank and a positive semi-definite diagonal matrix, is considered. We present a projection-type algorithm to address this problem. This algorithm appears to perform extremely well and is extremely fast even when the given covariance matrix has a very large dimension. The effectiveness of the algorithm is assessed through simulation studies and by applications to three real benchmark datasets that are considered. A local convergence analysis of the algorithm is also presented.
The problem of decomposing a given covariance matrix as the sum of a positive semi-definite matrix of given rank and a positive semi-definite diagonal matrix, is considered. We present a projection-type algorithm to address this problem. This algorithm appears to perform extremely well and is extremely fast even when the given covariance matrix has a very large dimension. The effectiveness of the algorithm is assessed through simulation studies and by applications to three real benchmark datasets that are considered. A local convergence analysis of the algorithm is also presented.
DOI : 10.14736/kyb-2019-4-0740
Classification : 62H25
Keywords: matrix decomposition; factor analysis; covariance matrices; low rank matrices; projections 
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     title = {An alternating minimization algorithm for {Factor} {Analysis}},
     journal = {Kybernetika},
     pages = {740--754},
     year = {2019},
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     number = {4},
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     language = {en},
     url = {http://geodesic.mathdoc.fr/articles/10.14736/kyb-2019-4-0740/}
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Ciccone, Valentina; Ferrante, Augusto; Zorzi, Mattia. An alternating minimization algorithm for Factor Analysis. Kybernetika, Tome 55 (2019) no. 4, pp. 740-754. doi: 10.14736/kyb-2019-4-0740

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