Using Non-negative Matrix Factorization for Bankruptcy Analysis

Chen, Ning; Ribeiro, Bernardete; Chen, An

Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/14961

Título:	Using Non-negative Matrix Factorization for Bankruptcy Analysis
Autor:	Chen, Ning Ribeiro, Bernardete Chen, An
Palavras-chave:	Bankruptcy analysis Clustering Non-negative matrix factorization Manifold
Publicador:	Universidade Federal de Lavras
Data:	1-Dez-2011
Referência:	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.
Outras Identificações :	http://www.dcc.ufla.br/infocomp/index.php/INFOCOMP/article/view/342
Descrição:	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.
Idioma:	eng
Aparece nas coleções:	Infocomp

Arquivos associados a este item:

Não existem arquivos associados a este item.

Mostrar registro completo do item Recomendar este item Visualizar estatísticas