diffusionGraph: Construction of diffusion graph

Description Usage Arguments References

View source: R/graphConstr.R

Description

Construct graph from data matrix based on diffusion maps (Coifman, 2015) with adaptive scaling according to local density.

Usage

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diffusionGraph(X, roots, k = 11, npc = min(100, dim(X) - 1),
  ndc = 40, s = 1, j = 7, lambda = 1e-04, sigma = NULL)

Arguments

X

data matrix (columns: n samples, rows: p variables)

roots

indices of columns of X or logical vector; start points of diffusion (i.e., the initial density is 1/n at specified n points).

k

integer; number of nearest neighbours to construct initial k-NN graph

npc

integer; number of principal components in calculating euclid distance.

ndc

integer; number of diffusion components in calculating diffusion distance.

s

numeric; time parameter of diffusion maps.

j

integer; use j-th nearest neighbouor in local scaling of sigma

lambda

numeric; ridge penalty in pulling back divergence

sigma

numeric; fixed sigma for isotropic diffusion

References

"Diffusion maps"

"Self-tuning spectral clustering"


kazumits/ddhodge documentation built on Oct. 17, 2019, 2:38 p.m.