Skip to contents

Generate a sparse dictionary with DGRDL

rs_sparse_dict_dgrdl.Rd

[Experimental] This is the Rust implementation of dual graph regularised dictionary learning in the implementation of Pan, et al., Cell Systems, 2022.

Usage

rs_sparse_dict_dgrdl(x, dgrdl_params, seed, verbose)

Arguments

x

Numerical matrix. Rows = samples, columns = features.

dgrdl_params

A list with the parameters for the algorithm. Missing items fall back to defaults. Expects the following items.

  • sparsity - Integer. Sparsity constraint (max non-zero coefficients per signal).

  • dict_size - Integer. Size of the dictionary.

  • alpha - Float. Sample context regularisation weight. The higher the stronger the regularisation.

  • beta - Float. Feature context regularisation weight. The higher the stronger the regularisation.

  • max_iter - Integer. Maximum iteration for the algorithm.

  • k_neighbours - Integer. Number of k neighbours for the sample and feature Laplacian matrix for the regularisation.

  • admm_iter - Integer. Number of iterations for using alternating direction method of multipliers (ADMM).

  • rho - Float. ADMM step size.

seed

Integer. Seed for the initialisation of the algorithm.

verbose

Boolean. Controls the verbosity of the function and reports timing of individual steps.

Value

A list with the following elements:

  • dictionary - The dictionary of samples x dict_size.

  • coefficients - The feature loadings of size dict_size x features.

  • feature_laplacian - The kNN graph Laplacian of the features as a CSR list with data, indptr, indices, nrow, ncol and cs_type.

  • sample_laplacian - The kNN graph Laplacian of the samples, same format.