Improving the efficiency of multidimensional scaling in the analysis of high-dimensional data using singular value decomposition

@article{Bcavin2011ImprovingTE,
  title={Improving the efficiency of multidimensional scaling in the analysis of high-dimensional data using singular value decomposition},
  author={Christophe B{\'e}cavin and Nicolas Tchitchek and Colette Mintsa-Eya and Annick Lesne and Arndt Benecke},
  journal={Bioinformatics},
  year={2011},
  volume={27 10},
  pages={1413-21}
}
MOTIVATION Multidimensional scaling (MDS) is a well-known multivariate statistical analysis method used for dimensionality reduction and visualization of similarities and dissimilarities in multidimensional data. The advantage of MDS with respect to singular value decomposition (SVD) based methods such as principal component analysis is its superior fidelity in representing the distance between different instances specially for high-dimensional geometric objects. Here, we investigate the… CONTINUE READING

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