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Internet Electronic Journal of Molecular Design - IEJMD, ISSN 1538-6414, CODEN IEJMAT
| ABSTRACT - Internet Electron. J. Mol. Des. March 2004, Volume 3, Number 3, 118-133 |
Structure-Activity Relationships using Locally Linear Embedding Assisted by
Support Vector and Lazy Learning Regressors
Rakesh Kumar, Abhijit Kulkarni, Valadi K. Jayaraman, and Bhaskar D. Kulkarni
Internet Electron. J. Mol. Des. 2004, 3, 118-133
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Abstract:
Structure-activity relationships are characterized by large
dimensions and conventional procedures become protracted while
modeling these relationships. To enhance the modeling abilities in
terms of reduced computational costs motivates the use of recently
developed tools in machine learning. Newly developed locally
linear embedding is used in reducing the nonlinear dimensions in
QSPR and QSAR. The reduced set is subsequently modeled with
robust regressors, namely lazy learning and support vector
regression. Both the datasets show improved results with the
reduced dimensions as compared to their original dimension
counterparts. Locally linear embedding for nonlinear
dimensionality reduction coupled with robust regressors such as
lazy learning and support vector regression seems to be a
promising option in analyzing the nonlinear datasets.
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