Supervised Isomap with Dissimilarity Measures in Embedding Learning

  • Bernardete Ribeiro
  • Armando Vieira
  • João Carvalho das Neves
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5197)

Abstract

In this paper we propose a supervised version of the Isomap algorithm by incorporating class label information into a dissimilarity matrix in a financial analysis setting. On the credible assumption that corporates financial status lie on a low dimensional manifold, nonlinear dimensionality reduction based on manifold learning techniques has strong potential for bankruptcy analysis in financial applications. We apply the method to a real data set of distressed and healthy companies for proper geometric tunning of similarity cases. We show that the accuracy of the proposed approach is comparable to the state-of-the-art Support Vector Machines (SVM) and Relevance Vector Machines (RVM) despite the fewer dimensions used resulting from embedding learning.

Keywords

Manifold Learning Financial Application 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Bernardete Ribeiro
    • 1
  • Armando Vieira
    • 2
  • João Carvalho das Neves
    • 3
  1. 1.Informatics Engineering DepartmentUniversity of CoimbraPortugal
  2. 2.Physics Department, Polytechnic Institute of PortoPortugal
  3. 3.ISEG - School of Economics and ManagementTech University of LisbonPortugal

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