An alternating minimization algorithm for Factor Analysis

Valentina Ciccone; Augusto Ferrante; Mattia Zorzi

Kybernetika (2019)

  • Volume: 55, Issue: 4, page 740-754
  • ISSN: 0023-5954

Abstract

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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.

How to cite

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Ciccone, Valentina, Ferrante, Augusto, and Zorzi, Mattia. "An alternating minimization algorithm for Factor Analysis." Kybernetika 55.4 (2019): 740-754. <http://eudml.org/doc/295067>.

@article{Ciccone2019,
abstract = {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.},
author = {Ciccone, Valentina, Ferrante, Augusto, Zorzi, Mattia},
journal = {Kybernetika},
keywords = {matrix decomposition; factor analysis; covariance matrices; low rank matrices; projections},
language = {eng},
number = {4},
pages = {740-754},
publisher = {Institute of Information Theory and Automation AS CR},
title = {An alternating minimization algorithm for Factor Analysis},
url = {http://eudml.org/doc/295067},
volume = {55},
year = {2019},
}

TY - JOUR
AU - Ciccone, Valentina
AU - Ferrante, Augusto
AU - Zorzi, Mattia
TI - An alternating minimization algorithm for Factor Analysis
JO - Kybernetika
PY - 2019
PB - Institute of Information Theory and Automation AS CR
VL - 55
IS - 4
SP - 740
EP - 754
AB - 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.
LA - eng
KW - matrix decomposition; factor analysis; covariance matrices; low rank matrices; projections
UR - http://eudml.org/doc/295067
ER -

References

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