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Learning Manifolds in Forensic Data

  • Frédéric Ratle
  • Anne-Laure Terrettaz-Zufferey
  • Mikhail Kanevski
  • Pierre Esseiva
  • Olivier Ribaux
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

Chemical data related to illicit cocaine seizures is analyzed using linear and nonlinear dimensionality reduction methods. The goal is to find relevant features that could guide the data analysis process in chemical drug profiling, a recent field in the crime mapping community. The data has been collected using gas chromatography analysis. Several methods are tested: PCA, kernel PCA, isomap, spatio-temporal isomap and locally linear embedding. ST-isomap is used to detect a potential time-dependent nonlinear manifold, the data being sequential. Results show that the presence of a simple nonlinear manifold in the data is very likely and that this manifold cannot be detected by a linear PCA. The presence of temporal regularities is also observed with ST-isomap. Kernel PCA and isomap perform better than the other methods, and kernel PCA is more robust than isomap when introducing random perturbations in the dataset.

Keywords

Locally Linear Embedding Kernel Principal Component Analysis Learn Manifold Linear Embedding Nonlinear Dimensionality Reduction 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Frédéric Ratle
    • 1
  • Anne-Laure Terrettaz-Zufferey
    • 2
  • Mikhail Kanevski
    • 1
  • Pierre Esseiva
    • 2
  • Olivier Ribaux
    • 2
  1. 1.Institut de Géomatique et d’Analyse du Risque, Faculté des Géosciences et de l’EnvironnementUniversité de LausanneAmphipôleSwitzerland
  2. 2.Institut de Police Scientifique et de Criminologie, Ecole des Sciences CriminellesUniversité de LausanneBatochimeSwitzerland

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