Generate sparse PCA-based feature projections for a list of voxel matrices

Applies sparse principal component analysis using the selected backend ("elasticnet", "PMA", or "sparsepca") to each matrix in a list of subject by voxel data.

antsr_spca_features(
  data_matrices,
  k,
  method = c("default", "elasticnet", "PMA", "sparsepca"),
  para = NULL
)

Arguments

data_matrices

A list of numeric matrices. Each matrix should be subjects by voxels.

k

Integer. Number of components to retain.

method

Character. Sparse PCA backend to use. One of "default", "elasticnet", "PMA", or "sparsepca".

para

Sparsity control parameter(s). Interpretation depends on backend: - For "elasticnet": number of nonzero loadings per component (length-k vector). - For "PMA": L1 bound on loading vector (scalar or length-k). - For "sparsepca": ignored (uses built-in defaults). - For "default": length-k vector of sparsity parameters.

Value

A named list of sparse projection matrices (voxels by k).