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| Format: | Artículo científico |
| Sprache: | en |
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Asociación Española para la Inteligencia Artificial
2008
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| Online-Zugang: | https://www.redalyc.org/articulo.oa?id=92503705 |
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Inhaltsangabe:
- Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data M. Strickert F. M. Schleif T. Villmann U. Seiffert Ingeniería correlation Feature rating data visualization In biomedical analytics one of the major criteria for the characterization of similarities between measureddata items is correlation. We demonstrate the use of the formal derivative of Pearson correlation for gradient-based optimization of data models. Firstly, individual data attributes can be rated according to their impacton pairwise data relationships, analogous to the variance measure in Euclidean space. Secondly, a versatilemethod is presented to perform faithful multi-dimensional scaling from a high-dimensional space of sourcedata to a low-dimensional target space, a method driven by maximization of correlation between distancesof static source data and adaptive target vectors. As shown for mass spectroscopy data, a combination ofattribute rating and data visualization helps revealing interesting data properties 2008 artículo científico 1137-3601 https://www.redalyc.org/articulo.oa?id=92503705 en http://www.redalyc.org/revista.oa?id=925 Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial application/pdf Asociación Española para la Inteligencia Artificial Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial (España) Num.37 Vol.12