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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...
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| Publicat a: | Biomed Res Int |
|---|---|
| Autors principals: | , |
| Format: | Artigo |
| Idioma: | Inglês |
| Publicat: |
Hindawi
2017
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| Matèries: | |
| Accés en línia: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5390636/ https://ncbi.nlm.nih.gov/pubmed/28466003 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1155/2017/1096028 |
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