Maf: Difference between revisions
imported>Scott (New page: ===Purpose=== Maximum Autocorrelative Factors. ===Synopsis=== :[scores,loads,mn,ssq] = maf(x) ===Description=== Similar to PCA, MAF uses singular value decomposition to provide a model of ...) |
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:[scores,loads,mn,ssq] = maf(x) | :[scores,loads,mn,ssq] = maf(x) | ||
===Description=== | ===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NOTE: The function MAF is obsolete and will be removed in future versions.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: | 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 | X_mn = scores\*loads | ||
Revision as of 10:05, 29 October 2009
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NOTE: The function MAF is obsolete and will be removed in future versions.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).