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| ===Purpose===
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| Maximum Autocorrelative Factors.
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| ===Synopsis===
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| :[scores,loads,mn,ssq] = maf(x)
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| ===Description===
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| NOTE: The function MAF is obsolete and will be removed in future versions. Please use [[maxautofactors]].
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| 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:
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| X_mn = scores\*loads
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| 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.
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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).
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| 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).
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| ===See Also===
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| [[mcr]], [[parafac]], [[pca]]
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Latest revision as of 11:44, 1 August 2019
