Unsupervised Machine Learning with Independent Component Analysis to Identify Areas of Progression in Glaucomatous Visual Fields

Purpose. To determine whether a variational Bayesian independent component analysis mixture model (vB-ICA-mm), a form of unsupervised machine learning, can be used to identify and quantify areas of progression in standard automated perimetry fields. Methods. In an earlier study, it was shown that a...

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Détails bibliographiques
Auteurs principaux: Sample, Pamela A., Boden, Catherine, Zhang, Zuohua, Pascual, John, Lee, Te-Won, Zangwill, Linda M., Weinreb, Robert N., Crowston, Jonathan G., Hoffmann, Esther M., Medeiros, Felipe A., Sejnowski, Terrence, Goldbaum, Michael
Format: Artigo
Langue:Inglês
Publié: 2005
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Article
Accès en ligne:https://ncbi.nlm.nih.gov/pmc/articles/PMC1832121/
https://ncbi.nlm.nih.gov/pubmed/16186350
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1167/iovs.04-1168
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