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Multivariate Direction Scoring for Dimensionality Reduction in Classification Problems

  • Giorgio Biagetti
  • Paolo CrippaEmail author
  • Laura Falaschetti
  • Simone Orcioni
  • Claudio Turchetti
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 56)

Abstract

Dimensionality reduction is the process of reducing the number of features in a data set. In a classification problem, the proposed formula allows to sort a set of directions to be used for data projection, according to a score that estimates their capability of discriminating the different data classes. A reduction in the number of features can be obtained by taking a subset of these directions and projecting data on this space. The projecting vectors can be derived from a spectral representation or other choices. If the vectors are eigenvectors of the data covariance matrix, the proposed score is aimed to take the place of the eigenvalues in eigenvector ordering.

Keywords

Principal Component Analysis Dimensionality Reduction Linear Discriminant Analysis Classification Problem Speaker Identification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Giorgio Biagetti
    • 1
  • Paolo Crippa
    • 1
    Email author
  • Laura Falaschetti
    • 1
  • Simone Orcioni
    • 1
  • Claudio Turchetti
    • 1
  1. 1.DII—Dipartimento di Ingegneria dell’InformazioneUniversità Politecnica delle MarcheAnconaItaly

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