Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Schizophrenia Imaging Genetics Data Via Multiple Kernel
  Canonical Correlation Analysis   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 CCA represent new avenues for studying
schizophrenia, because, to our knowledge, these methods have not been used on
these data before. Classification is performed via k-means clustering on the
kernel matrix outputs of the Kernel and Multiple Kernel CCA algorithm.
Accuracies of the Kernel and Multiple Kernel CCA classification are compared to
that of the regularized linear CCA algorithm classification, and are found to
be significantly more accurate. Both algorithms demonstrate maximal accuracies
when the combination of DNA methylation and fMRI data are used, and experience
lower accuracies when the SNP data are incorporated.

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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg
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The categories of this paper are: cond-mat
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: cs
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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-th
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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom