An alternating minimization algorithm for Factor Analysis

Title:

An alternating minimization algorithm for Factor Analysis

Creator:

Ciccone, Valentina, Ferrante , Augusto, and Zorzi, Mattia

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:7fd4a147-1eb5-4ba7-be12-a6e202cf3a0f
uuid:7fd4a147-1eb5-4ba7-be12-a6e202cf3a0f
doi:10.14736/kyb-2019-4-0740

Subject:

matrix decomposition, factor analysis, covariance matrices, low rank matrices, and projections

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

740-754

Source:

Kybernetika | 2019 Volume:55 | Number:4

Harvested from:

CDK

Metadata only:

false