Manifold locality constrained low-rank representation and its applications

You, Congzhe; Wu, Xiao-Jun; Palade, Vasile; Altahhan, Abdulrahman

doi:10.1109/ijcnn.2016.7727616

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…where ∥Z∥ 1 is the l 1 -norm to guarantee the sparsity of coefficient matrix. In real-world applications, the sparsity and nonnegativity matrix Z obtained by the NNLRS method can offer a basis for semi-supervised learning by constructing the discriminative and informative graph (You et al, 2016).…”

Section: Non-negative Lrr With Sparsitymentioning

confidence: 99%

“…The LRR method can find a low-rank matrix to capture and represent the global structure of the raw dataset (Liu et al, 2010). The key to the LRR method is that the high-dimensional data can be represented by potential low-dimensional subspaces (You et al, 2016). In bioinformatics, LRR has achieved great success in gene expression data mining.…”

Section: Introductionmentioning

confidence: 99%

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Non-Negative Symmetric Low-Rank Representation Graph Regularized Method for Cancer Clustering Based on Score Function

Wang

Liu

et al. 2020

Front. Genet.

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As an important approach to cancer classification, cancer sample clustering is of particular importance for cancer research. For high dimensional gene expression data, examining approaches to selecting characteristic genes with high identification for cancer sample clustering is an important research area in the bioinformatics field. In this paper, we propose a novel integrated framework for cancer clustering known as the non-negative symmetric low-rank representation with graph regularization based on score function (NSLRG-S). First, a lowest rank matrix is obtained after NSLRG decomposition. The lowest rank matrix preserves the local data manifold information and the global data structure information of the gene expression data. Second, we construct the Score function based on the lowest rank matrix to weight all of the features of the gene expression data and calculate the score of each feature. Third, we rank the features according to their scores and select the feature genes for cancer sample clustering. Finally, based on selected feature genes, we use the K-means method to cluster the cancer samples. The experiments are conducted on The Cancer Genome Atlas (TCGA) data. Comparative experiments demonstrate that the NSLRG-S framework can significantly improve the clustering performance.

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Section: Non-negative Lrr With Sparsitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%