tucker
Purpose
TUCKER analysis for n-way arrays.
Synopsis
model = tucker(x,ncomp,initval,options)
%tucker model
pred = tucker(x,model) %application
options = tucker('options')
Description
TUCKER
decomposes an array of order K (where K >= 3) into the summation over the outer product of K
vectors. As opposed to PARAFAC every combination of factors in each mode are
included (subspaces). Missing values must be NaN or Inf.
INPUTS:
x = the multi-way array to be decomposed and
ncomp = the number of components to estimate, or
model = a TUCKER
model structure.
OPTIONAL INPUTS:
initval = if initval is the loadings from a previous TUCKER model are
then these are used as the initial starting values to estimate a final model,
if initval is a TUCKER
model structure then mode 1 loadings (scores) are estimated from x and the
loadings in the other modes (see output pred),
options = discussed below.
OUTPUTS:
model = a structure array with the following fields:
modeltype: 'TUCKER',
datasource: structure
array with information about input data,
date: date
of creation,
time: time
of creation,
info: additional
model information,
loads: 1
by K+1 cell array with model loadings for each mode/dimension,
pred: cell
array with model predictions for each input data block,
tsqs: cell
array with T2 values for each mode,
ssqresiduals: cell
array with sum of squares residuals for each mode,
description: cell
array with text description of model, and
detail: sub-structure
with additional model details and results.
pred = is a structure array, similar to model, that contains
prediction results for new data fit to the TUCKER model.
Options
options = a structure array with the following fields:
name: 'options', name indicating that this is an options structure,
display: [
{'on'} | 'off' ], governs level of display,
plots: [ {'final'} | 'all' | 'none' ], governs level of plotting,
weights: [], used for fitting a weighted loss function
(discussed below),
stopcrit: [1e-6
1e-6 10000 3600] defines the stopping criteria as [(relative tolerance)
(absolute tolerance) (maximum number of iterations) (maximum time in seconds)],
init: [ 0 ], defines how parameters are initialized (see PARAFAC),
line: [ 0 | {1}] defines whether to use the line search {default uses
it},
algo: this option is not yet active,
blockdetails: 'standard'
missdat: this option is not yet active,
samplemode: [1], defines which mode
should be considered the sample or object mode and
constraints: {4x1
cell}, defines constraints on parameters (see PARAFAC). The first three cells
define constraints on loadings whereas the last cell defines constraints on the
core.
The default options can be retreived using: options = tucker('options');.
See Also
corcondia, coreanal, corecalc, datahat, gram, mpca, mwfit, outerm, parafac, parafac2, tld, unfoldm
