DSTPCA: Double-Sparse Constrained Tensor Principal Component Analysis Method for Feature Selection

The identification of differentially expressed genes plays an increasingly important role biologically. Therefore, the feature selection approach has attracted much attention in the field of bioinformatics. The most popular method of principal component analysis studies two-dimensional data without...

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Vydané v:	IEEE/ACM transactions on computational biology and bioinformatics Ročník 18; číslo 4; s. 1481 - 1491
Hlavní autori:	Hu, Yue, Liu, Jin-Xing, Gao, Ying-Lian, Shang, Junliang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.07.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Bioinformatics Computational Biology - methods Constraints Differentially expressed genes Diseases Feature extraction Feature selection Gene Expression Profiling - methods Genes Humans Linear programming Mathematical analysis Matrix decomposition Neoplasms - genetics Neoplasms - metabolism Objective function Principal Component Analysis Principal components analysis Regularization sparse constraint Sparse matrices tensor principal component analysis Tensors Two dimensional analysis
ISSN:	1545-5963, 1557-9964, 1557-9964
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