Dimensionality reduction using singular vectors

Abstract A common problem in machine learning and pattern recognition is the process of identifying the most relevant features, specifically in dealing with high-dimensional datasets in bioinformatics. In this paper, we propose a new feature selection method, called Singular-Vectors Feature Selectio...

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Autores principales:	Majid Afshar, Hamid Usefi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4e673d78ae7e4591840721580ab9e0f4
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spelling	oai:doaj.org-article:4e673d78ae7e4591840721580ab9e0f42021-12-02T14:03:44ZDimensionality reduction using singular vectors10.1038/s41598-021-83150-y2045-2322https://doaj.org/article/4e673d78ae7e4591840721580ab9e0f42021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83150-yhttps://doaj.org/toc/2045-2322Abstract A common problem in machine learning and pattern recognition is the process of identifying the most relevant features, specifically in dealing with high-dimensional datasets in bioinformatics. In this paper, we propose a new feature selection method, called Singular-Vectors Feature Selection (SVFS). Let $$D= [A \mid \mathbf {b}]$$ D = [ A ∣ b ] be a labeled dataset, where $$\mathbf {b}$$ b is the class label and features (attributes) are columns of matrix A. We show that the signature matrix $$S_A=I-A^{\dagger }A$$ S A = I - A † A can be used to partition the columns of A into clusters so that columns in a cluster correlate only with the columns in the same cluster. In the first step, SVFS uses the signature matrix $$S_D$$ S D of D to find the cluster that contains $$\mathbf {b}$$ b . We reduce the size of A by discarding features in the other clusters as irrelevant features. In the next step, SVFS uses the signature matrix $$S_A$$ S A of reduced A to partition the remaining features into clusters and choose the most important features from each cluster. Even though SVFS works perfectly on synthetic datasets, comprehensive experiments on real world benchmark and genomic datasets shows that SVFS exhibits overall superior performance compared to the state-of-the-art feature selection methods in terms of accuracy, running time, and memory usage. A Python implementation of SVFS along with the datasets used in this paper are available at https://github.com/Majid1292/SVFS .Majid AfsharHamid UsefiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Majid Afshar Hamid Usefi Dimensionality reduction using singular vectors
description	Abstract A common problem in machine learning and pattern recognition is the process of identifying the most relevant features, specifically in dealing with high-dimensional datasets in bioinformatics. In this paper, we propose a new feature selection method, called Singular-Vectors Feature Selection (SVFS). Let $$D= [A \mid \mathbf {b}]$$ D = [ A ∣ b ] be a labeled dataset, where $$\mathbf {b}$$ b is the class label and features (attributes) are columns of matrix A. We show that the signature matrix $$S_A=I-A^{\dagger }A$$ S A = I - A † A can be used to partition the columns of A into clusters so that columns in a cluster correlate only with the columns in the same cluster. In the first step, SVFS uses the signature matrix $$S_D$$ S D of D to find the cluster that contains $$\mathbf {b}$$ b . We reduce the size of A by discarding features in the other clusters as irrelevant features. In the next step, SVFS uses the signature matrix $$S_A$$ S A of reduced A to partition the remaining features into clusters and choose the most important features from each cluster. Even though SVFS works perfectly on synthetic datasets, comprehensive experiments on real world benchmark and genomic datasets shows that SVFS exhibits overall superior performance compared to the state-of-the-art feature selection methods in terms of accuracy, running time, and memory usage. A Python implementation of SVFS along with the datasets used in this paper are available at https://github.com/Majid1292/SVFS .
format	article
author	Majid Afshar Hamid Usefi
author_facet	Majid Afshar Hamid Usefi
author_sort	Majid Afshar
title	Dimensionality reduction using singular vectors
title_short	Dimensionality reduction using singular vectors
title_full	Dimensionality reduction using singular vectors
title_fullStr	Dimensionality reduction using singular vectors
title_full_unstemmed	Dimensionality reduction using singular vectors
title_sort	dimensionality reduction using singular vectors
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4e673d78ae7e4591840721580ab9e0f4
work_keys_str_mv	AT majidafshar dimensionalityreductionusingsingularvectors AT hamidusefi dimensionalityreductionusingsingularvectors
_version_	1718392077495042048

Dimensionality reduction using singular vectors

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