Semi-supervised kernel canonical correlation analysis with application to human fMRI
<p>Kernel canonical correlation analysis (KCCA) is a general technique for subspace learning that incorporates principal components analysis (PCA) and Fisher linear discriminant analysis (LDA) as special cases. By finding directions that maximize correlation, KCCA learns representations that a...
| Autori principali: | , , , , |
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| Natura: | Journal article |
| Lingua: | English |
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Elsevier
2011
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