Gene Feature Extraction Based on Nonnegative Dual Graph Regularized Latent Low-Rank Representation.
作者： Guoliang Yang , Zhengwei Hu
DOI: 10.1155/2017/1096028
关键词: Cluster analysis 、 Dual graph 、 Redundancy (engineering) 、 Representation (mathematics) 、 Computer science 、 Algorithm 、 Feature extraction 、 Subspace topology 、 Computational complexity theory 、 Rank (graph theory)
摘要: 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 basis (Lat-LRR). By introducing manifold constraint, NNDGLLRR can keep internal spatial structure original data effectively improve final clustering accuracy while segmenting subspace. The introduction constraints makes computation with some sparsity, which enhances robustness algorithm. Different from Lat-LRR, solution adopted to simplify computational complexity. experimental results show that proposed algorithm has good performance for noise profile, which, compared LRR achieve better accuracy.
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core.ac.uk参考文章(30)
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