Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Second order accurate distributed eigenvector computation for extremely
  large matrices   We propose a second-order accurate method to estimate the eigenvectors of
extremely large matrices thereby addressing a problem of relevance to
statisticians working in the analysis of very large datasets. More
specifically, we show that averaging eigenvectors of randomly subsampled
matrices efficiently approximates the true eigenvectors of the original matrix
under certain conditions on the incoherence of the spectral decomposition. This
incoherence assumption is typically milder than those made in matrix completion
and allows eigenvectors to be sparse. We discuss applications to spectral
methods in dimensionality reduction and information retrieval.

21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics