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Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation
Aiming at the problem of gene expression profile's high redundancy and heavy noise, a new feature extraction model based on nonnegative dual graph regularized latent low-rank representation (NNDGLLRR) is presented on the basis of latent low-rank representation (Lat-LRR). By introducing dual gra...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Hindawi
2017
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390636/ https://www.ncbi.nlm.nih.gov/pubmed/28466003 http://dx.doi.org/10.1155/2017/1096028 |