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Robust subspace methods for outlier detection in genomic data circumvents the curse of dimensionality
The application of machine learning to inference problems in biology is dominated by supervised learning problems of regression and classification, and unsupervised learning problems of clustering and variants of low-dimensional projections for visualization. A class of problems that have not gained...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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The Royal Society
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7062061/ https://www.ncbi.nlm.nih.gov/pubmed/32257299 http://dx.doi.org/10.1098/rsos.190714 |