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 three algorithms: - Divide-and-conquer MDS proposed by Delicado P. and C. Pachón-García (2021) . - Interpolation MDS, also proposed by Delicado P. and C. Pachón-García (2021) , which uses Gower's interpolation formula as described in Gower, J. C. and D. J. Hand (1995). - Fast MDS, which is an implementation of the algorithm proposed by Yang, T., J. Liu, L. McMillan, and W. Wang (2006). The main idea of these algorithms is based on partitioning the data set into small pieces, where classical methods can work. In order to align all the solutions, Procrustes formula is used as described in Borg, I. and P. Groenen (2005).


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2.0.1 by Cristian Pachón García, 2 months ago

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Authors: Cristian Pachón García [aut, cre] , Pedro Delicado [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports stats, parallel

Suggests testthat

See at CRAN