TY - JOUR A2 - Liu, Gang AU - Yang, Guoliang AU - Hu, Zhengwei PY - 2017 DA - 2017/03/30 TI - Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation SP - 1096028 VL - 2017 AB - 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 graph manifold regularized constraint, the NNDGLLRR can keep the internal spatial structure of the original data effectively and improve the final clustering accuracy while segmenting the subspace. The introduction of nonnegative constraints makes the computation with some sparsity, which enhances the robustness of the algorithm. Different from Lat-LRR, a new solution model is adopted to simplify the computational complexity. The experimental results show that the proposed algorithm has good feature extraction performance for the heavy redundancy and noise gene expression profile, which, compared with LRR and Lat-LRR, can achieve better clustering accuracy. SN - 2314-6133 UR - https://doi.org/10.1155/2017/1096028 DO - 10.1155/2017/1096028 JF - BioMed Research International PB - Hindawi KW - ER -