An iterative projections algorithm for ML factor analysis

Abd Krim Seghouane

doi:10.1109/MLSP.2008.4685502

An iterative projections algorithm for ML factor analysis

Abd Krim Seghouane^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Citations (Scopus)

Abstract

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 converge. A simulation example that illustrates the effectiveness of the proposed algorithm for ML factor analysis is presented.

Original language	English
Title of host publication	Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Pages	333-338
Number of pages	6
DOIs	https://doi.org/10.1109/MLSP.2008.4685502
Publication status	Published - 2008
Event	2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 - Cancun, Mexico Duration: 16 Oct 2008 → 19 Oct 2008

Publication series

Name	Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Conference

Conference	2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008
Country/Territory	Mexico
City	Cancun
Period	16/10/08 → 19/10/08

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10.1109/MLSP.2008.4685502

Cite this

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title = "An iterative projections algorithm for ML factor analysis",

abstract = "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 converge. A simulation example that illustrates the effectiveness of the proposed algorithm for ML factor analysis is presented.",

author = "Seghouane, {Abd Krim}",

year = "2008",

doi = "10.1109/MLSP.2008.4685502",

language = "English",

isbn = "9781424423767",

series = "Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008",

pages = "333--338",

booktitle = "Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008",

note = "2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008 ; Conference date: 16-10-2008 Through 19-10-2008",

}

Seghouane, AK 2008, An iterative projections algorithm for ML factor analysis. in Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008., 4685502, Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, pp. 333-338, 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008, Cancun, Mexico, 16/10/08. https://doi.org/10.1109/MLSP.2008.4685502

An iterative projections algorithm for ML factor analysis. / Seghouane, Abd Krim.
Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008. 2008. p. 333-338 4685502 (Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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 converge. A simulation example that illustrates the effectiveness of the proposed algorithm for ML factor analysis is presented.

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U2 - 10.1109/MLSP.2008.4685502

DO - 10.1109/MLSP.2008.4685502

M3 - Conference contribution

SN - 9781424423767

T3 - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

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EP - 338

BT - Proceedings of the 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

T2 - 2008 IEEE Workshop on Machine Learning for Signal Processing, MLSP 2008

Y2 - 16 October 2008 through 19 October 2008

ER -

An iterative projections algorithm for ML factor analysis

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