Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Estimating the principal components of correlation matrices from all
  their empirical eigenvectors   We consider the problem of estimating the principal components of a
population correlation matrix from a limited number of measurement data. Using
a combination of random matrix and information-theoretic tools, we show that
all the eigenmodes of the sample correlation matrices are informative, and not
only the top ones. We show how this information can be exploited when prior
information about the principal component, such as whether it is localized or
not, is available by mapping the estimation problem onto the search for the
ground state of a spin-glass-like effective Hamiltonian encoding the prior.
Results are illustrated numerically on the spiked covariance model.

37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
11
The categories of this paper are: hep-lat
1
The categories of this paper are: cs
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ