Proposals:Statistics Framework Runtime Vector Size: Difference between revisions
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= Rationale for having run time length = | = Rationale for having run time length = | ||
For algorithms such as Normalized cuts [1] and other Kernel PCA feature space projection techniques [2], it may be necessary to keep the dimensionality of the feature space as a variable. This requires removing MeasurementVectorSize as a static method and making it an iVar. | |||
[1] PAMI - Vol26, No2, Spectral Grouping using the Nystrom method , Feb 2004 | |||
[2] Neural Computation - Nonlinear component analysis as a Kernel Eigenvalue problem, vol 10, 1998 | |||
= Proposed Implementation Plan = | = Proposed Implementation Plan = | ||
= Proposed Transition Plan = | = Proposed Transition Plan = | ||
Revision as of 20:23, 5 July 2005
Refactoring the Statistics Framework to have Runtime Length
Currently, the Statistics Framework requires the MeasurementVector to have a length defined at compile time.
Rationale for having compile time length
The statistics classes in ITK have MeasurementVectorSize (length of each measurement vector) as a static const value. This has until now been sufficient since typical statistics operations involve sampling an image where the number of measurement vectors is a variable, but the measurement vector size is usually fixed and depends on the dimension of the parametric space.
Rationale for having run time length
For algorithms such as Normalized cuts [1] and other Kernel PCA feature space projection techniques [2], it may be necessary to keep the dimensionality of the feature space as a variable. This requires removing MeasurementVectorSize as a static method and making it an iVar.
[1] PAMI - Vol26, No2, Spectral Grouping using the Nystrom method , Feb 2004 [2] Neural Computation - Nonlinear component analysis as a Kernel Eigenvalue problem, vol 10, 1998
