Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Finding the needle in high-dimensional haystack: A tutorial on canonical
  correlation analysis   Since the beginning of the 21st century, the size, breadth, and granularity
of data in biology and medicine has grown rapidly. In the example of
neuroscience, studies with thousands of subjects are becoming more common,
which provide extensive phenotyping on the behavioral, neural, and genomic
level with hundreds of variables. The complexity of such big data repositories
offer new opportunities and pose new challenges to investigate brain,
cognition, and disease. Canonical correlation analysis (CCA) is a prototypical
family of methods for wrestling with and harvesting insight from such rich
datasets. This doubly-multivariate tool can simultaneously consider two
variable sets from different modalities to uncover essential hidden
associations. Our primer discusses the rationale, promises, and pitfalls of CCA
in biomedicine.

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The categories of this paper are: hep-ph
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The categories of this paper are: supr-con
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: alg-geom
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The categories of this paper are: q-fin
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: quant-ph
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The categories of this paper are: solv-int
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The categories of this paper are: atom-ph