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A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection
BACKGROUND: Clustering and feature selection act major roles in many communities. As a matrix factorization, Low-Rank Representation (LRR) has attracted lots of attentions in clustering and feature selection, but sometimes its performance is frustrated when the data samples are insufficient or conta...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8772046/ https://www.ncbi.nlm.nih.gov/pubmed/35057728 http://dx.doi.org/10.1186/s12859-021-04333-y |
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author | Liu, Qi |
author_facet | Liu, Qi |
author_sort | Liu, Qi |
collection | PubMed |
description | BACKGROUND: Clustering and feature selection act major roles in many communities. As a matrix factorization, Low-Rank Representation (LRR) has attracted lots of attentions in clustering and feature selection, but sometimes its performance is frustrated when the data samples are insufficient or contain a lot of noise. RESULTS: To address this drawback, a novel LRR model named TGLRR is proposed by integrating the truncated nuclear norm with graph-Laplacian. Different from the nuclear norm minimizing all singular values, the truncated nuclear norm only minimizes some smallest singular values, which can dispel the harm of shrinkage of the leading singular values. Finally, an efficient algorithm based on Linearized Alternating Direction with Adaptive Penalty is applied to resolving the optimization problem. CONCLUSIONS: The results show that the TGLRR method exceeds the existing state-of-the-art methods in aspect of tumor clustering and gene selection on integrated gene expression data. |
format | Online Article Text |
id | pubmed-8772046 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87720462022-01-20 A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection Liu, Qi BMC Bioinformatics Research BACKGROUND: Clustering and feature selection act major roles in many communities. As a matrix factorization, Low-Rank Representation (LRR) has attracted lots of attentions in clustering and feature selection, but sometimes its performance is frustrated when the data samples are insufficient or contain a lot of noise. RESULTS: To address this drawback, a novel LRR model named TGLRR is proposed by integrating the truncated nuclear norm with graph-Laplacian. Different from the nuclear norm minimizing all singular values, the truncated nuclear norm only minimizes some smallest singular values, which can dispel the harm of shrinkage of the leading singular values. Finally, an efficient algorithm based on Linearized Alternating Direction with Adaptive Penalty is applied to resolving the optimization problem. CONCLUSIONS: The results show that the TGLRR method exceeds the existing state-of-the-art methods in aspect of tumor clustering and gene selection on integrated gene expression data. BioMed Central 2022-01-20 /pmc/articles/PMC8772046/ /pubmed/35057728 http://dx.doi.org/10.1186/s12859-021-04333-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Liu, Qi A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection |
title | A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection |
title_full | A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection |
title_fullStr | A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection |
title_full_unstemmed | A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection |
title_short | A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection |
title_sort | truncated nuclear norm and graph-laplacian regularized low-rank representation method for tumor clustering and gene selection |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8772046/ https://www.ncbi.nlm.nih.gov/pubmed/35057728 http://dx.doi.org/10.1186/s12859-021-04333-y |
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