Maf: Difference between revisions
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Input X is either an Image DataSet object or a three-way double matrix (first two dimensions are spatial, last is variable). | Input X is either an 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 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). | Output ssq is an experimental sum of squares captured table but because 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). | ||
MAF is accessible in the Analysis window by choosing the PCA analysis method, then clicking on the toolbar icon "Edit Analysis Method Options" and changing the "algorithm" setting from "svd" to "maf", and click "Ok". | |||
===See Also=== | ===See Also=== | ||
[[mcr]], [[parafac]], [[pca]] | [[mcr]], [[parafac]], [[pca]] | ||
Latest revision as of 10:24, 26 May 2015
Purpose
Maximum Autocorrelative Factors.
Synopsis
- [scores,loads,mn,ssq] = maf(x)
Description
NOTE: The function MAF is obsolete and will be removed in future versions. Please use maxautofactors.
Similar to PCA, MAF uses singular value decomposition to provide a model of data (x) which captures maximum spatially-correlated variance. 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 an 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 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).
MAF is accessible in the Analysis window by choosing the PCA analysis method, then clicking on the toolbar icon "Edit Analysis Method Options" and changing the "algorithm" setting from "svd" to "maf", and click "Ok".