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A Novel Multivariate Mapping Method for Analyzing High-Dimensional Numerical Datasets

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 9728)


In modern science, dealing with high dimensional datasets is a very common task due to the increasing availability of data. Multivariate data analysis represents challenges in both theoretical and empirical levels. Until now, several methods for dimensionality reduction like Principal Component Analysis, Low Variance Filter and High Correlated Columns has been proposed. However, sometimes the reduction achieved by existing methods is not accurate enough to analyze datasets where, for practical reasons, more reduction of the original dataset is required. In this paper, we propose a new method to transform high dimensional dataset into a one-dimensional. We show that such transformation preserves the properties of the original dataset and thus, it can be suitable for many applications where a high reduction is required.


  • Feature selection
  • Dimensionality reduction
  • Density estimation

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The authors acknowledge the support of Consejo Nacional de Ciencia y tecnología (CONACyT) and Centro de Investigación y Estudios Avanzados-CINVESTAV.

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Correspondence to Edwin Aldana-Bobadilla .

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Aldana-Bobadilla, E., Molina-Villegas, A. (2016). A Novel Multivariate Mapping Method for Analyzing High-Dimensional Numerical Datasets. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2016. Lecture Notes in Computer Science(), vol 9728. Springer, Cham.

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