Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Smooth Principal Component Analysis over two-dimensional manifolds with
  an application to Neuroimaging   Motivated by the analysis of high-dimensional neuroimaging signals located
over the cortical surface, we introduce a novel Principal Component Analysis
technique that can handle functional data located over a two-dimensional
manifold. For this purpose a regularization approach is adopted, introducing a
smoothing penalty coherent with the geodesic distance over the manifold. The
model introduced can be applied to any manifold topology, can naturally handle
missing data and functional samples evaluated in different grids of points. We
approach the discretization task by means of finite element analysis and
propose an efficient iterative algorithm for its resolution. We compare the
performances of the proposed algorithm with other approaches classically
adopted in literature. We finally apply the proposed method to resting state
functional magnetic resonance imaging data from the Human Connectome Project,
where the method shows substantial differential variations between brain
regions that were not apparent with other approaches.

15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
13
The categories of this paper are: quant-ph