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Kernel and Multiple Kernel Canonical Correlation Analysis (CCA) are employed to classify schizophrenic and healthy patients based on their SNPs, DNA Methylation and fMRI data. Kernel and Multiple Kernel CCA are popular methods for finding nonlinear correlations between high-dimensional datasets. Data was gathered from 183 patients, 79 with schizophrenia and 104 healthy controls. Kernel and Multiple...
Kernel canonical correlation analysis (kernel CCA) is sensitive to the choice of appropriate kernels and associated parameters. To the best of our knowledge there is no general well-founded approach for choosing them. As we demonstrate with Gaussian kernels, the kernel CCA tends to show perfect correlation as the bandwidth parameter of the Gaussian kernel decreases, while it provides inappropriate...
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