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独立主成分分析与主成分分析代码,速度比较快,而且比较好用-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 small
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下载文件列表
train_test.m
FastICA_25/Contents.m
FastICA_25/CVS/
FastICA_25/CVS/Entries
FastICA_25/CVS/Repository
FastICA_25/CVS/Root
FastICA_25/demosig.m
FastICA_25/dispsig.m
FastICA_25/fastica.m
FastICA_25/fasticag.m
FastICA_25/fpica.m
FastICA_25/gui_adv.m
FastICA_25/gui_advc.m
FastICA_25/gui_cb.m
FastICA_25/gui_cg.m
FastICA_25/gui_help.m
FastICA_25/gui_l.m
FastICA_25/gui_lc.m
FastICA_25/gui_s.m
FastICA_25/gui_sc.m
FastICA_25/icaplot.m
FastICA_25/pcamat.m
FastICA_25/remmean.m
FastICA_25/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
FastICA_25/Contents.m
FastICA_25/CVS/
FastICA_25/CVS/Entries
FastICA_25/CVS/Repository
FastICA_25/CVS/Root
FastICA_25/demosig.m
FastICA_25/dispsig.m
FastICA_25/fastica.m
FastICA_25/fasticag.m
FastICA_25/fpica.m
FastICA_25/gui_adv.m
FastICA_25/gui_advc.m
FastICA_25/gui_cb.m
FastICA_25/gui_cg.m
FastICA_25/gui_help.m
FastICA_25/gui_l.m
FastICA_25/gui_lc.m
FastICA_25/gui_s.m
FastICA_25/gui_sc.m
FastICA_25/icaplot.m
FastICA_25/pcamat.m
FastICA_25/remmean.m
FastICA_25/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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