In these first experiments, both ICA and whitened PCA are used to compress the data, and all the components are used for classifying the examples. The classifier used is a 1-NN with Euclidean distance. The results shown in next sections are clear: when a rotational invariant classifier is used (as 1-NN with L2-norm) the classification results of FastICA/whitened PCA are equivalent, while the difference between Infomax and whitened data es significant but sma

train_test.m
FastICA_25\Contents.m
..........\CVS
..........\...\Entries
..........\...\Repository
..........\...\Root
..........\demosig.m
..........\dispsig.m
..........\fastica.m
..........\fasticag.m
..........\fpica.m
..........\gui_adv.m
..........\gui_advc.m
..........\gui_cb.m
..........\gui_cg.m
..........\gui_help.m
..........\gui_l.m
..........\gui_lc.m
..........\gui_s.m
..........\gui_sc.m
..........\icaplot.m
..........\pcamat.m
..........\remmean.m
..........\whitenv.m
FastICA_25
classifier.m
data_artificialset.m
data_coil_100.m
data_orl_faces.m
example.m
example_loop.m
extraction.m
knn.m
posact.m
read_cross_validation.m
runica.m
separate_train_test.m
sortem.m

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