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. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Ribeiro, B., Vieira, A., & Carvalho Das Neves, J. (2008). Supervised Isomap with dissimilarity measures in embedding learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5197 LNCS, pp. 389–396). https://doi.org/10.1007/978-3-540-85920-8_48
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