Sparse factor analysis via likelihood and ℓ 1-regularization

Lipeng Ning, Tryphon T. Georgiou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

In this note we consider the basic problem to identify linear relations in noise. We follow the viewpoint of factor analysis (FA) where the data is to be explained by a small number of independent factors and independent noise. Thereby an approximation of the sample covariance is sought which can be factored accordingly. An algorithm is proposed which weighs in an ℓ 1-regularization term that induces sparsity of the linear model (factor) against a likelihood term that quantifies distance of the model to the sample covariance. The algorithm compares favorably against standard techniques of factor analysis. Their performance is compared first by simulation, where ground truth is available, and then on stock-market data where the proposed algorithm gives reasonable and sparser models.

Original languageEnglish (US)
Title of host publication2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
Pages5188-5192
Number of pages5
DOIs
StatePublished - Dec 1 2011
Event2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011 - Orlando, FL, United States
Duration: Dec 12 2011Dec 15 2011

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Other

Other2011 50th IEEE Conference on Decision and Control and European Control Conference, CDC-ECC 2011
CountryUnited States
CityOrlando, FL
Period12/12/1112/15/11

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