setup_SpectralLaplace: Setup SpectralLaplaceConfig

In rtemis: Schema-Driven Machine Learning and Visualization

Setup SpectralLaplaceConfig

Description

Setup a SpectralLaplaceConfig object for spectral clustering with an exponential (Laplacian) kernel, via the 'kernlab' package. The exponential kernel decays more slowly than the Gaussian of setup_SpectralRBF and so keeps more weight on distant pairs.

Usage

setup_SpectralLaplace(
  k = 3L,
  sigma = NULL,
  iterations = 200L,
  nystrom = NULL,
  features = NULL
)

Arguments

k	Integer [2, Inf): Number of clusters. Must be no more than the number of cases.
sigma	Optional Numeric (0, Inf): Inverse kernel width. Unset uses 1.
iterations	Integer [1, Inf): Maximum number of k-means iterations run on the spectral embedding.
nystrom	Optional NystromConfig: Approximate the affinity matrix from a sample of the cases, from setup_Nystrom. NULL decomposes it in full.
features	Optional Character vector: Names of at least 2 distinct feature columns to cluster on. NULL clusters on all numeric features.

Details

Argument names are rtemis' own. They map to kernlab::specc() as: k -> centers, sigma -> kpar = list(sigma) with kernel = "laplacedot", nystrom -> nystrom.red and nystrom.sample; iterations keeps its name.

Value

SpectralLaplaceConfig object.

Author(s)

EDG

References

Ng AY, Jordan MI, Weiss Y (2001). On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14, 849-856.

Examples

setup_SpectralLaplace(k = 3L, sigma = 0.5)

rtemis documentation built on Oct. 20, 2026, 1:06 a.m.