Abstract
In this paper we propose a generalization to symbolic interval valued variables, of the Principal Curves and Surfaces method proposed by Hastie in [6]. Given a data set X with n observations and m continuous variables, the main idea of Principal Curves and Surfaces method is to generalize the principal component line, providing a smooth one-dimensional curved approximation to a set of data points in Rm. A principal surface is more general, providing a curved manifold approximation of dimension 2 or more. In our case we are interested in finding the main principal curve that approximates better symbolic interval data variables. In [3,4], authors proposed the Centers Method and the Vertices Method to extend the well-known principal components analysis method to a particular kind of symbolic objects characterized by multivalued variables of interval type. In this paper we generalize both, the Centers Method and the Vertices Method, finding a smooth curve that passes through the middle of the data X in an orthogonal sense. Some comparisons of the proposed method regarding the Centers and the Vertices Methods are made, this was done with the RSDA package using Ichino data set, see [1,10]. To make these comparisons we have used the correlation index.
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Jorge Arce, G., & Oldemar Rodríguez, R. (2016). Principal curves and surfaces to interval valued variables. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10022 LNAI, pp. 297–309). Springer Verlag. https://doi.org/10.1007/978-3-319-47955-2_25
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