Supervised Isomap with Dissimilarity Measures in Embedding Learning

Authors:
Bernardete Ribeiro;Armando Vieira;João Carvalho Das Neves
Affiliations:
Informatics Engineering Department, University of Coimbra, Portugal;Physics Department, Polytechnic Institute of Porto, Portugal;ISEG - School of Economics and Management, Tech University of Lisbon, Portugal
Venue:
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Year:
2008

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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.