University of Oulu

Saberi-Movahed, F., Rostami, M., Berahmand, K., Karami, S., Tiwari, P., Oussalah, M., & Band, S. S. (2022). Dual regularized unsupervised feature selection based on matrix factorization and minimum redundancy with application in gene selection. Knowledge-Based Systems, 256, 109884.

Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy with application in gene selection

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Author: Saberi-Movahed, Farid1; Rostami, Mehrdad2; Berahmand, Kamal3;
Organizations: 1Department of Applied Mathematics, Faculty of Sciences and Modern Technologies, Graduate University of Advanced Technology, Kerman, Iran
2Centre of Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland
3School of Computer Science, Faculty of Science, Queensland University of Technology (QUT), Organization, Brisbane, Australia
4Department of Mathematics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, 45137-66731, Iran
5School of Information Technology, Halmstad University, Sweden
6Research Unit of Medical Imaging, Physics, and Technology, Faculty of Medicine, University of Oulu, Finland
7Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Taiwan
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 3.5 MB)
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Language: English
Published: Elsevier, 2022
Publish Date: 2022-11-25


Gene expression data have become increasingly important in machine learning and computational biology over the past few years. In the field of gene expression analysis, several matrix factorization-based dimensionality reduction methods have been developed. However, such methods can still be improved in terms of efficiency and reliability. In this paper, an innovative approach to feature selection, called Dual Regularized Unsupervised Feature Selection Based on Matrix Factorization and Minimum Redundancy (DR-FS-MFMR), is introduced. The major focus of DR-FS-MFMR is to discard redundant features from the set of original features. In order to reach this target, the primary feature selection problem is defined in terms of two aspects: (1) the matrix factorization of data matrix in terms of the feature weight matrix and the representation matrix, and (2) the correlation information related to the selected features set. Then, the objective function is enriched by employing two data representation characteristics along with an inner product regularization criterion to perform both the redundancy minimization process and the sparsity task more precisely. To demonstrate the proficiency of the DR-FS-MFMR method, a large number of experimental studies are conducted on nine gene expression datasets. The obtained computational results indicate the efficiency and productivity of DR-FS-MFMR for the gene selection task.

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Series: Knowledge-based systems
ISSN: 0950-7051
ISSN-E: 1872-7409
ISSN-L: 0950-7051
Volume: 256
Article number: 109884
DOI: 10.1016/j.knosys.2022.109884
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
217 Medical engineering
Copyright information: © 2022 The Author(s). This is an open access article under the CC BY license (