bigmds
Multidimensional Scaling for Big Data
MDS is a statistic tool for reduction of dimensionality, using as input a distance matrix of dimensions n × n. When n is large, classical algorithms suffer from computational problems and MDS configuration can not be obtained. With this package, we address these problems by means of six algorithms, being two of them original proposals: - Landmark MDS proposed by De Silva V. and JB. Tenenbaum (2004). - Interpolation MDS proposed by Delicado P. and C. Pachón-García (2021) doi:10.48550/arXiv.2007.11919 (original proposal). - Reduced MDS proposed by Paradis E (2018). - Pivot MDS proposed by Brandes U. and C. Pich (2007) - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021) doi:10.48550/arXiv.2007.11919 (original proposal). - Fast MDS, proposed by Yang, T., J. Liu, L. McMillan and W. Wang (2006).
- Version3.0.0
- R versionunknown
- LicenseMIT
- LicenseLICENSE
- Needs compilation?No
- bigmds citation info
- Last release01/09/2024
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Team
Cristian Pachón García
Pedro Delicado
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- Imports3 packages
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