Run diffusion map

Performs non-linear dimensionality reduction using the exact dense method. Best suited for smaller datasets where calculating the full N x N distance matrix fits safely into memory.

Usage

run_diffusion(data, sigma = 1, dims = 2, oversampling = 10, n_iter = 10)

Arguments

data

Numeric matrix or data.frame. Missing values (NA) and infinite values are not supported and will cause an error.

sigma

Numeric. Bandwidth for Gaussian kernel (default 1.0).

dims

Integer. Number of diffusion dimensions to return (default 2).

oversampling

Integer. Dimensions added for stability in randomized SVD algorithm (default 10). Higher values may negatively impact results of thr algorithm.

n_iter

Integer. Number of subspace iterations in randomized SVD algorithm (default 10).

Value

A diffmap object

Examples

data <- matrix(runif(1000), nrow = 50, ncol = 20)
optimal_sigma <- estimate_sigma(data)
model <- run_diffusion(data, sigma = optimal_sigma)