Using Non-negative Matrix Factorization for Bankruptcy Analysis
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/342 http://repositorio.ufla.br/jspui/handle/1/14961 |
Resumo: | Dimensionality reduction is demonstrated crucial to improve the predictive capability of models by means of linear or nonlinear projections. Non-negative matrix factorization (NMF) is a popular multivariate analysis technique for part-based data representation. It attempts to find an approximation of a high dimensional matrix as the product of two low dimensional matrices under the non-negative constraint. Recently a graph regularized non-negative matrix factorization (GNMF) provides a formal way to incorporate the geometrical structure into the NMF decomposition, particularly applicable to the data embedded in submanifolds of the Euclidean space. In this paper, the usage of GNMF in financial analysis is discussed from the perspectives of unsupervised clustering and supervised classification. Experimental results on a French bankruptcy data set show the potential of GNMF on data representation. |
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Using Non-negative Matrix Factorization for Bankruptcy AnalysisBankruptcy analysisClusteringNon-negative matrix factorizationManifoldDimensionality reduction is demonstrated crucial to improve the predictive capability of models by means of linear or nonlinear projections. Non-negative matrix factorization (NMF) is a popular multivariate analysis technique for part-based data representation. It attempts to find an approximation of a high dimensional matrix as the product of two low dimensional matrices under the non-negative constraint. Recently a graph regularized non-negative matrix factorization (GNMF) provides a formal way to incorporate the geometrical structure into the NMF decomposition, particularly applicable to the data embedded in submanifolds of the Euclidean space. In this paper, the usage of GNMF in financial analysis is discussed from the perspectives of unsupervised clustering and supervised classification. Experimental results on a French bankruptcy data set show the potential of GNMF on data representation.Universidade Federal de Lavras2011-12-012017-08-01T21:08:39Z2017-08-01T21:08:39Z2017-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/342CHEN, N.; RIBEIRO, B.; CHEN, A. Using Non-negative Matrix Factorization for Bankruptcy Analysis. INFOCOMP Journal of Computer Science, Lavras, v. 10, n. 4, p. 57-64, Dec. 2011.http://repositorio.ufla.br/jspui/handle/1/14961INFOCOMP; Vol 10 No 4 (2011): December, 2011; 57-641982-33631807-4545reponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttp://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/342/326Copyright (c) 2016 INFOCOMP Journal of Computer Scienceinfo:eu-repo/semantics/openAccessChen, NingRibeiro, BernardeteChen, An2021-02-05T12:13:56Zoai:localhost:1/14961Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2021-02-05T12:13:56Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)false |
dc.title.none.fl_str_mv |
Using Non-negative Matrix Factorization for Bankruptcy Analysis |
title |
Using Non-negative Matrix Factorization for Bankruptcy Analysis |
spellingShingle |
Using Non-negative Matrix Factorization for Bankruptcy Analysis Chen, Ning Bankruptcy analysis Clustering Non-negative matrix factorization Manifold |
title_short |
Using Non-negative Matrix Factorization for Bankruptcy Analysis |
title_full |
Using Non-negative Matrix Factorization for Bankruptcy Analysis |
title_fullStr |
Using Non-negative Matrix Factorization for Bankruptcy Analysis |
title_full_unstemmed |
Using Non-negative Matrix Factorization for Bankruptcy Analysis |
title_sort |
Using Non-negative Matrix Factorization for Bankruptcy Analysis |
author |
Chen, Ning |
author_facet |
Chen, Ning Ribeiro, Bernardete Chen, An |
author_role |
author |
author2 |
Ribeiro, Bernardete Chen, An |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Chen, Ning Ribeiro, Bernardete Chen, An |
dc.subject.por.fl_str_mv |
Bankruptcy analysis Clustering Non-negative matrix factorization Manifold |
topic |
Bankruptcy analysis Clustering Non-negative matrix factorization Manifold |
description |
Dimensionality reduction is demonstrated crucial to improve the predictive capability of models by means of linear or nonlinear projections. Non-negative matrix factorization (NMF) is a popular multivariate analysis technique for part-based data representation. It attempts to find an approximation of a high dimensional matrix as the product of two low dimensional matrices under the non-negative constraint. Recently a graph regularized non-negative matrix factorization (GNMF) provides a formal way to incorporate the geometrical structure into the NMF decomposition, particularly applicable to the data embedded in submanifolds of the Euclidean space. In this paper, the usage of GNMF in financial analysis is discussed from the perspectives of unsupervised clustering and supervised classification. Experimental results on a French bankruptcy data set show the potential of GNMF on data representation. |
publishDate |
2011 |
dc.date.none.fl_str_mv |
2011-12-01 2017-08-01T21:08:39Z 2017-08-01T21:08:39Z 2017-08-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/342 CHEN, N.; RIBEIRO, B.; CHEN, A. Using Non-negative Matrix Factorization for Bankruptcy Analysis. INFOCOMP Journal of Computer Science, Lavras, v. 10, n. 4, p. 57-64, Dec. 2011. http://repositorio.ufla.br/jspui/handle/1/14961 |
url |
http://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/342 http://repositorio.ufla.br/jspui/handle/1/14961 |
identifier_str_mv |
CHEN, N.; RIBEIRO, B.; CHEN, A. Using Non-negative Matrix Factorization for Bankruptcy Analysis. INFOCOMP Journal of Computer Science, Lavras, v. 10, n. 4, p. 57-64, Dec. 2011. |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
http://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/342/326 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 INFOCOMP Journal of Computer Science |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Lavras |
publisher.none.fl_str_mv |
Universidade Federal de Lavras |
dc.source.none.fl_str_mv |
INFOCOMP; Vol 10 No 4 (2011): December, 2011; 57-64 1982-3363 1807-4545 reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
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1807835080292302848 |