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LJELSR: A Strengthened Version of JELSR for Feature Selection and Clustering

1
School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China
2
Bio-Computing Research Center, Harbin Institute of Technology, Shenzhen 518055, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(4), 886; https://doi.org/10.3390/ijms20040886
Received: 4 December 2018 / Revised: 11 January 2019 / Accepted: 7 February 2019 / Published: 18 February 2019
(This article belongs to the Section Molecular Informatics)
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Abstract

Feature selection and sample clustering play an important role in bioinformatics. Traditional feature selection methods separate sparse regression and embedding learning. Later, to effectively identify the significant features of the genomic data, Joint Embedding Learning and Sparse Regression (JELSR) is proposed. However, since there are many redundancy and noise values in genomic data, the sparseness of this method is far from enough. In this paper, we propose a strengthened version of JELSR by adding the L1-norm constraint on the regularization term based on a previous model, and call it LJELSR, to further improve the sparseness of the method. Then, we provide a new iterative algorithm to obtain the convergence solution. The experimental results show that our method achieves a state-of-the-art level both in identifying differentially expressed genes and sample clustering on different genomic data compared to previous methods. Additionally, the selected differentially expressed genes may be of great value in medical research. View Full-Text
Keywords: differentially expressed genes; feature selection; L1-norm; sample clustering; sparse constraint differentially expressed genes; feature selection; L1-norm; sample clustering; sparse constraint
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MDPI and ACS Style

Wu, S.-S.; Hou, M.-X.; Feng, C.-M.; Liu, J.-X. LJELSR: A Strengthened Version of JELSR for Feature Selection and Clustering. Int. J. Mol. Sci. 2019, 20, 886.

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