spatial_constraints: Construct a Sparse Matrix of Spatial Constraints for Data...

In bbuchsbaum/neighborweights: Constructing Adjacency Matrices Based on Spatial and Feature Similarity

Construct a Sparse Matrix of Spatial Constraints for Data Blocks

Description

This function creates a sparse matrix of spatial constraints for a set of data blocks. The spatial constraints matrix is useful in applications like image segmentation, where spatial information is crucial for identifying different regions in the image.

Usage

spatial_constraints(
  coords,
  nblocks = 1,
  sigma_within = 5,
  sigma_between = 1,
  shrinkage_factor = 0.1,
  nnk_within = 27,
  nnk_between = 1,
  weight_mode_within = "heat",
  weight_mode_between = "binary",
  variable_weights = 1,
  verbose = FALSE
)

Arguments

coords	The spatial coordinates as a matrix with rows as objects and columns as dimensions; or as a list of matrices where each element of the list contains the coordinates for a block.
nblocks	The number of coordinate blocks. Default is 1.
sigma_within	The bandwidth of the within-block smoother. Default is 5.
sigma_between	The bandwidth of the between-block smoother. Default is 1.
shrinkage_factor	The amount of shrinkage towards the spatial block average. Default is 0.1.
nnk_within	The maximum number of nearest neighbors for within-block smoother. Default is 27.
nnk_between	The maximum number of nearest neighbors for between-block smoother. Default is 1.
weight_mode_within	The within-block nearest neighbor weight mode ("heat" or "binary"). Default is "heat".
weight_mode_between	The between-block nearest neighbor weight mode ("heat" or "binary"). Default is "binary".
variable_weights	A vector of per-variable weights. Default is 1.
verbose	A boolean indicating whether to print progress messages. Default is FALSE.

Value

A sparse matrix representing the spatial constraints for the provided data blocks.

Details

The function computes within-block and between-block constraints based on the provided coordinates, bandwidths, and other input parameters. It then balances the within-block and between-block constraints using a shrinkage factor, and normalizes the resulting matrix by the first eigenvalue.

bbuchsbaum/neighborweights documentation built on April 1, 2024, 8:41 p.m.