Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Pyrcca: regularized kernel canonical correlation analysis in Python and
  its applications to neuroimaging   Canonical correlation analysis (CCA) is a valuable method for interpreting
cross-covariance across related datasets of different dimensionality. There are
many potential applications of CCA to neuroimaging data analysis. For instance,
CCA can be used for finding functional similarities across fMRI datasets
collected from multiple subjects without resampling individual datasets to a
template anatomy. In this paper, we introduce Pyrcca, an open-source Python
module for executing CCA between two or more datasets. Pyrcca can be used to
implement CCA with or without regularization, and with or without linear or a
Gaussian kernelization of the datasets. We demonstrate an application of CCA
implemented with Pyrcca to neuroimaging data analysis. We use CCA to find a
data-driven set of functional response patterns that are similar across
individual subjects in a natural movie experiment. We then demonstrate how this
set of response patterns discovered by CCA can be used to accurately predict
subject responses to novel natural movie stimuli.

11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
34
The categories of this paper are: chem-ph
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics