Inheritance diagram for nipy.modalities.fmri.pca:
This module provides a class for principal components analysis (PCA).
PCA is an orthonormal, linear transform (i.e., a rotation) that maps the data to a new coordinate system such that the maximal variability of the data lies on the first coordinate (or the first principal component), the second greatest variability is projected onto the second coordinate, and so on. The resulting data has unit covariance (i.e., it is decorrelated). This technique can be used to reduce the dimensionality of the data.
More specifically, the data is projected onto the eigenvectors of the covariance matrix.
Bases: object
Compute the PCA of an image (over axis=0). Image coordmap should have a subcoordmap method.
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Perform the computations needed for the PCA. This stores the covariance/correlation matrix of the data in the attribute ‘C’. The components are stored as the attributes ‘components’, for an fMRI image these are the time series explaining the most variance.
Returns: | None |
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Output the component images – by default, only output the first principal component.
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Returns: | TODO |
Parameters: |
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Returns: | TODO |