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Locally Linear Embedding for dimensionality reduction in QSAR

  • P.-J. L’Heureux
  • J. Carreau
  • Y.  Bengio
  • O. Delalleau
  • S. Y. Yue
Article

Abstract

Current practice in Quantitative Structure Activity Relationship (QSAR) methods usually involves generating a great number of chemical descriptors and then cutting them back with variable selection techniques. Variable selection is an effective method to reduce the dimensionality but may discard some valuable information. This paper introduces Locally Linear Embedding (LLE), a local non-linear dimensionality reduction technique, that can statistically discover a low-dimensional representation of the chemical data. LLE is shown to create more stable representations than other non-linear dimensionality reduction algorithms, and to be capable of capturing non-linearity in chemical data.

Keywords

kernel methods neural network QSAR spectral dimensionality reduction 

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Copyright information

© Springer 2004

Authors and Affiliations

  • P.-J. L’Heureux
    • 1
  • J. Carreau
    • 1
  • Y.  Bengio
    • 1
  • O. Delalleau
    • 1
  • S. Y. Yue
    • 2
  1. 1.DIRO, Université MontréalMontréalCanada
  2. 2.AstraZeneca R&DMontrealCanada

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