Influence Function and Robust Variant of Kernel Canonical Correlation Analysis
Many unsupervised kernel methods rely on the estimation of kernel covariance operator (kernel CO) or kernel cross-covariance operator (kernel CCO). Both are sensitive to contaminated data, even when bounded positive definite kernels are used. To the best of our knowledge, there are few well-founded...
| Veröffentlicht in: | Neurocomputing. - 1998. - 304(2018) vom: 23. Aug., Seite 12-29 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2018
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| Zugriff auf das übergeordnete Werk: | Neurocomputing |
| Schlagworte: | Journal Article Influence function Kernel (coss-) covariance operator Kernel methods Robustness and Imaging genetics analysis |
| Online verfügbar |
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