Description Usage Arguments Value Note Examples
Computes the multiview spectral clustering of data on a list of matrices or dist objects (or a mix of both), supposed to be different views of the same data.
1 |
x |
A list of feature matrices or |
k |
Number of desired clusters. |
sigmas |
Either |
neighbours |
Either |
clustering |
Tells |
A list with four elements: clustering
is a vector of integers with the
clustering assignment of each sample (not included if clustering = FALSE
),
evalues
is a matrix with the eigenvalues
of the common principal components (CPC) step, evectors
is a matrix with the
eigenvectors of the CPC step, and sigmas
is a vector with the sigmas used on
the Gaussian radial basis function of each input view.
All input views must have the same number of samples (rows).
1 2 3 |
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