| tbmClustering {tensorsparse} | R Documentation |
Perform tensor clustering via tensor block model (TBM)
Description
Perform tensor clustering via tensor block model (TBM) method.
Usage
tbmClustering(
x,
k,
r,
l,
lambda = 0,
max.iter = 1000,
threshold = 1e-10,
sim.times = 1,
trace = FALSE,
Cs.init = NULL,
Ds.init = NULL,
Es.init = NULL,
method = "L0"
)
Arguments
x |
an order-3 data tensor |
k |
an positive integer, the numbers of clusters at mode 1 |
r |
an positive integer, the numbers of clusters at mode 2 |
l |
an positive integer, the numbers of clusters at mode 3 |
lambda |
a numeric value, regularization coefficient |
max.iter |
a positive integer, the maximum numbers of iteration |
threshold |
a positive small numeric value for convergence threshold |
sim.times |
the number of simulation replicates when performing clustering |
trace |
logic value, print result per each iteration if TRUE |
Cs.init |
vector or NULL, initial cluster label assignment at mode 1 |
Ds.init |
vector or NULL, initial cluster label assignment at mode 2 |
Es.init |
vector or NULL, initial cluster label assignment at mode 3 |
method |
two options: "L0", "L1". "L0" indicates L0 penalty, and "L1" indicates Lasso penalty |
Value
a list
judgeX estimated underlying signal tensor
Cs clustering result at mode 1
Ds clustering result at mode 2
Es clustering result at mode 3
mus estimated block means
Author(s)
Yuchen Zeng yzeng58@wisc.edu
References
M. Wang and Y. Zeng, "Multiway clustering via tensoe block models". Advances in Neural Information Processing System 32 (NeurIPS), 715-725, 2019.
Examples
x = getOrder3Tensor(20,20,20,2,2,2)$x
tbmClustering(x,2,2,2)