A mapping defines transformation of objects from one representation (e.g. raw data or an initial vector space) to another (e.g. a lower dimensional vector space or class labels). There are several types of mappings, fixed mappings, trainable mappings, and special mappings for combining with other mappings or the generation of data. Here they are summarized.
There are five mapping types shortly described in the below table.
C stand for data, usually to be supplied as a dataset or a datafile. Sometimes also matrices of doubles or cell arrays are allowed (see cells and doubles). Fixed mappings are denoted by
F. Trainable mappings show themselves either as untrained (
U) or trained (
G is a generator,
V a combiner and
W an arbitrary mapping.
|C = A*F||Fixed mappings
|T = B*U||An untrained mapping
||A trained mapping
||A generator mapping G generates from a given dataset A a new dataset C in the same space.|
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.