A fixed mapping transforms one vector space into another in a data independent way. Its operation just depends on some user defined parameter settings. An example is the sigmoid scaling:
F = sigm(,s) in which s defines the smoothness of the function. It is called by
B = A*F in which
A is an input dataset and
B is the transformed result.
The following rules apply if a dataset
A is processed by a sequential combination of a fixed mapping
F with another fixed mapping
,an untrained mapping
U or a trained mapping
W is an arbitrary mapping.
||This is the same as
||Fixed mapping, it generates as well|
||Generator, the data is transformed by a fixed mapping.|
||The untrained mapping
An example is:
U = pcam(,10)*ldc;
T = A*(im_resize(,32,32)*U)
The untrained mapping defines a mapping on the first 10 principal components and performs in this space a linear classifier assuming normal class densities. Training preceded by resizing all images to 32*32 pixels in order to make the images comparable is .
A can be a dataset as well as a datafile.
cells and doubles
operations: datasets datafiles cells and doubles mappings classifiers stacked parallel sequential dyadic.
user commands: datasets representation classifiers evaluation clustering examples support routines.
introductory examples: Introduction Scatterplots Datasets Datafiles Mappings Classifiers Evaluation Learning curves Feature curves Dimension reduction Combining classifiers Dissimilarities.