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arXiv · 1806.04433

An alternating minimization algorithm for Factor Analysis

Valentina Ciccone·Augusto Ferrante·Mattia Zorzi
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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 datasets that are considered as benchmark for the problem. A local convergence analysis of the algorithm is also presented.

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Valentina Ciccone, Augusto Ferrante, Mattia Zorzi. 2018-06-12. An alternating minimization algorithm for Factor Analysis. https://arxiv.org/abs/1806.04433

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