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...

Descripción completa

Detalles Bibliográficos
Autores principales: Yang, Guoliang, Hu, Zhengwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Research Article
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

Internet

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

Ejemplares similares