ArticleBioinformatics (Oxford, England)2010
Penalized mixtures of factor analyzers with application to clustering high-dimensional microarray data.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Mixture of regressions with multivariate responses for discovering subtypes in Alzheimer's biomarkers with detection limits.Data science in science · 2024Article
- Clustering High-Dimensional Landmark-based Two-dimensional Shape DataJournal of the American Statistical Association · 2015Article
- Cancer subtype discovery and biomarker identification via a new robust network clustering algorithm.PloS one · 2013Article
- Penalized model-based clustering with unconstrained covariance matrices.Electronic journal of statistics · 2009Article
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3 authors.
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Abstract
motivationModel-based clustering has been widely used, e.g. in microarray data analysis. Since for high-dimensional data variable selection is necessary, several penalized model-based clustering methods have been proposed tørealize simultaneous variable selection and clustering. However, the existing methods all assume that the variables are independent with the use of diagonal covariance matrices.
resultsTo model non-independence of variables (e.g. correlated gene expressions) while alleviating the problem with the large number of unknown parameters associated with a general non-diagonal covariance matrix, we generalize the mixture of factor analyzers to that with penalization, which, among others, can effectively realize variable selection. We use simulated data and real microarray data to illustrate the utility and advantages of the proposed method over several existing ones.
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