Maxautofactors: Difference between revisions
imported>Scott (New page: ===Purpose=== Maximum / Principal Autocorrelation Factors. ===Synopsis=== :[model] = maxautofactors(x,ncomp,options) ===Description=== In it's default mode, MAXAUTOFACTORS uses a gene...) |
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====Outputs==== | ====Outputs==== | ||
The resulting scores (scores), loadings (loads), and mean spectrum (mn) can be used to reconstruct the mean-centered data matrix X_mn | The resulting scores (scores), loadings (loads), and mean spectrum (mn) can be used to reconstruct the mean-centered data matrix X_mn | ||
:X_mn = scores*loads | :<tt>X_mn = scores*loads</tt> | ||
The difference between PCA and MAF is that MAF extracts loadings which are highly correlated in the spatial dimension of an image. In addition, MAF always returns the entire set of components up to the rank of the data matrix. Input X is either a Image DataSet object or a three-way double matrix (first two dimensions are spatial, last is variable). Output ssq is an experimental sum of squares captured table but because of the nature of the decomposition, this table is only approximate. It includes the component number (column 1), estimated eigenvalue (column 2), and the estimated % captured per variable and total (columns 3 and 4, respectively). | The difference between PCA and MAF is that MAF extracts loadings which are highly correlated in the spatial dimension of an image. In addition, MAF always returns the entire set of components up to the rank of the data matrix. Input X is either a Image DataSet object or a three-way double matrix (first two dimensions are spatial, last is variable). Output ssq is an experimental sum of squares captured table but because of the nature of the decomposition, this table is only approximate. It includes the component number (column 1), estimated eigenvalue (column 2), and the estimated % captured per variable and total (columns 3 and 4, respectively). | ||
===Options=== | ===Options=== | ||
Revision as of 14:53, 29 September 2009
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.
Inputs
- x = MxNxP image class 'dataset' or 'double'.
- ncomp = number of components (integer).
Outputs
The resulting scores (scores), loadings (loads), and mean spectrum (mn) can be used to reconstruct the mean-centered data matrix X_mn
- X_mn = scores*loads
The difference between PCA and MAF is that MAF extracts loadings which are highly correlated in the spatial dimension of an image. In addition, MAF always returns the entire set of components up to the rank of the data matrix. Input X is either a Image DataSet object or a three-way double matrix (first two dimensions are spatial, last is variable). Output ssq is an experimental sum of squares captured table but because of the nature of the decomposition, this table is only approximate. It includes the component number (column 1), estimated eigenvalue (column 2), and the estimated % captured per variable and total (columns 3 and 4, respectively).
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' | 'manual']