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 data-sets that are considered. A local convergence analysis of the algorithm is also presented.

An alternating minimization algorithm for Factor Analysis

Ciccone, Valentina;Ferrante, Augusto;Zorzi, Mattia
2019

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 data-sets that are considered. A local convergence analysis of the algorithm is also presented.
2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3315975
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