Supervoxel Clustering in Time — supervoxel_cluster

Cluster feature matrix (rows = time points) in a "supervoxel" style but over temporal dimension.

Usage

supervoxel_cluster_time(
  feature_mat,
  K = min(nrow(feature_mat), 100),
  sigma1 = 1,
  sigma2 = 3,
  iterations = 50,
  TR = 2,
  filter = list(lp = 0, hp = 0),
  use_medoid = FALSE,
  nreps = 5
)

Arguments

feature_mat: A matrix (nrows = time points, ncols = features) or vice versa.
K: Number of clusters.
sigma1: Heat kernel bandwidth for feature similarity (data vectors).
sigma2: Heat kernel bandwidth for spatial similarity (coordinate vectors).
iterations: Maximum number of cluster iterations.
TR: Repetition time (seconds).
filter: List specifying optional frequency filters, e.g., list(lp=0.1, hp=0).
use_medoid: Whether to use medoids for cluster centers.
nreps: Number of repeated initializations.

Value

A list of cluster results (one per repetition), each of which has the same structure as supervoxel_cluster_fit().

Examples

if (FALSE) { # \dontrun{
feature_mat <- matrix(rnorm(100 * 10), 100, 10)
library(future)
plan(multicore)
cres <- supervoxel_cluster_time(t(feature_mat), K=5)
} # }