The multiview package provides multiview methods to work with multiview data (datasets with several data matrices from the same samples). It contains methods for multiview dimensionality reduction and methods for multiview clustering.
Multiview dimensionality reduction
Given a multiview dataset with v input data matrices, multiview dimensionality reduction methods produce a single, low-dimensional projection of the input data samples, trying to mantain as much of the original information as possible.
Package multiview offers the function mvmds to perform multiview dimensionality reduction in a similar way than the multidimensional scaling method (cmdscale).
Another dimensionality reduction function in this package is mvtsne, that extends tsne to multiview data.
Multiview clustering
Given a multiview dataset with v input data matrices, multiview clustering methods produce a single clustering assignment, considering the information from all the input views. Package multiview offers the function mvsc to perform multiview spectral clustering. It is an extension to spectral clustering (specc) to multiview datasets.
⇓ Download multiview package for R
⇓ Multiview package for Python
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