Compresses voxel features, selects low-gradient seeds, and assigns voxels by
deterministic multi-source Dijkstra propagation. Edges are exactly the
requested masked-grid neighbors; excluded gaps are never bridged. The edge
objective is alpha * feature_distance + (1 - alpha) * physical_distance / compactness.
Usage
cluster4d_g3s(
vec,
mask,
K = 100,
n_components = 15,
variance_threshold = 0.95,
alpha = 0.5,
compactness = NULL,
max_refinement_iter = 3,
verbose = FALSE,
use_irlba = TRUE,
use_rsvd = TRUE,
connectivity = 26
)Arguments
- vec
A
NeuroVecorSparseNeuroVeccontaining the input features.- mask
A
NeuroVol; exactly finite values greater than zero are included.- K
Positive requested cluster count.
- n_components
Requested SVD feature count.
- variance_threshold
Minimum retained variance in
[0, 1].- alpha
Feature-distance weight in
[0, 1]; zero is spatial-only and one is feature-only.- compactness
Positive finite physical length scale. Smaller values impose a stronger spatial penalty. The default uses masked voxel measure, K, and the effective dimension, so single-slice masks remain well-defined.
- max_refinement_iter
Non-negative number of boundary passes. Refinement uses the same feature/spatial mixture and final labels are repaired through the shared adjacency-preserving exact-K engine.
- verbose
Whether to report phase progress.
- use_irlba
Whether large SVDs may use
irlba.- use_rsvd
Whether randomized SVD may be used when available.
- connectivity
Exact grid connectivity: 6, 18, or 26.