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SuSiE PCA: A scalable Bayesian variable selection technique for principal component analysis
Latent factor models, like principal component analysis (PCA), provide a statistical framework to infer low-rank representation in various biological contexts. However, feature selection is challenging when this low-rank structure manifests from a sparse subspace. We introduce SuSiE PCA, a scalable...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Elsevier
2023
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10638022/ https://www.ncbi.nlm.nih.gov/pubmed/37953948 http://dx.doi.org/10.1016/j.isci.2023.108181 |