decomp_symmetric: Decompose Large and Sparse Matrix
In UtrechtUniversity/SCCA: Spectral Clustering Correspondence Analysis in R

Description Usage Arguments Details References

Computes Eigenvalues of the similarity matrix. As input, the contingency matrix M should be given. The similarity matrix and associated eigenvectors and eigenvalues are determined internally. See for a elaborate description van Dam, et al, 2021

1	decomp_symmetric(matrix, max_eigenvalues, decomp = "svd")

`matrix`	Incidence matrix (e.g. species - location), which can be interpreted as the bi-adjacency matrix of a bipartite network.
`max_eigenvalues`	Max. number of eigenvalues to compute. Default is 25.
`decomp`	The decomposition to use: svd (default) or svds. The later only computes the k leading singular values and vectors of a rectangular matrix

If both dimensions of data matrix are greater than max_eigenvalues, then the number of computed eigenvalues is restricted to max_eigenvalues; otherwise, the shortest dimension is chosen.

svds uses the rARPACK implementation of the singular value decomposition for efficient approximation of singular decomposition for large sparse matrices.

van Dam, et al. (2021), Correspondence analysis, spectral clustering and graph embedding: applications to ecology and economic complexity; *name of journal*; DOI: <doi>.

UtrechtUniversity/SCCA documentation built on April 16, 2021, 3:23 a.m.