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An iterative projections algorithm for ML factor analysis

Seghouane, Abd-Krim

Description

Alternating minimization of the infonnation divergence is used to derive an effective algorithm for maximum likelihood (ML) factor analysis. The proposed algorithm is derived as an iterative alternating projections procedure on a model family of probability distributions defined on the factor analysis model and a desired family of probability distributions constrained to be concentrated on the observed data. The algorithm presents the advantage of being simple to implement and stable to...[Show more]

CollectionsANU Research Publications
Date published: 2008
Type: Conference paper
URI: http://hdl.handle.net/1885/50255
Source: Proceedings of IEEE Workshop on Machine Learning for Signal Processing 2008
DOI: 10.1109/MLSP.2008.4685502

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