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Penalized model-based clustering with unconstrained covariance matrices
Clustering is one of the most useful tools for high-dimensional analysis, e.g., for microarray data. It becomes challenging in presence of a large number of noise variables, which may mask underlying clustering structures. Therefore, noise removal through variable selection is necessary. One effecti...
Tallennettuna:
| Päätekijät: | , , |
|---|---|
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
2009
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC2867492/ https://ncbi.nlm.nih.gov/pubmed/20463857 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1214/09-EJS487 |
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