Maxautofactors: Difference between revisions
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====Outputs==== | ====Outputs==== | ||
* '''model''': standard model structure containing the MAF model (see MODELSTRUCT). | |||
: | * '''options''': options structure. (some fields may have been modified) | ||
===Options=== | ===Options=== |
Revision as of 09:40, 29 September 2011
Purpose
Maximum / Principal Autocorrelation Factors.
Synopsis
- [model] = maxautofactors(x,ncomp,options)
Description
In it's default mode, MAXAUTOFACTORS uses a generalized eigenvalue decomposition to provide a model of data (x) which captures maximum spatially-correlated variance. An approximate solution is used to stabelize and speed up the algorithm (see options.varcap).
Inputs
- x = MxNxP image class 'dataset' or 'double'.
- ncomp = number of components (integer).
Outputs
- model: standard model structure containing the MAF model (see MODELSTRUCT).
- options: options structure. (some fields may have been modified)
Options
options = a structure array with the following fields:
- display: [ 'off' | {'on'} ] governs level of display to command window.
- plots: [ 'none' | {'final'} ] governs level of plotting.
- algorithm: [ {'maf'} | 'paf' | 'mdf' | 'pdf' ]
- if algorithm == 'maf' or 'paf' the options settings are for numerator and denomenator operators to be I and the first difference respectively.
- if algorithm == 'mdf' or 'pdf' the options settings are for numerator and denomenator operators to be 1stD and the 2ndD respectively.
- varcap: [{0.999}] 0<varcap<1, specifies the variance of X to be captured when approximating the input X with a PCA model.
- If (varcap) is an integer >=ncomp, this is the number of PCs used. The minimum number is (ncomp).
- smooth: [ ] smoothness penalty, based on the fraction of variance of the numerator (typical value might be 1e-3 to 0.05).
- Smoothness is only available for MAF and MDF.