Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Computation of Robust Subspace Estimators   Dimension reduction is often an important step in the analysis of
high-dimensional data. PCA is a popular technique to find the best
low-dimensional approximation of high-dimensional data. However, classical PCA
is very sensitive to atypical data. Robust methods to estimate the
low-dimensional subspace that best approximates the regular data have been
proposed. However, for high-dimensional data his algorithms become
computationally expensive. Alternative algorithms for the robust subspace
estimators are proposed that are better suited to compute the solution for
high-dimensional problems. The main ingredients of the new algorithms are
twofold. First, the principal directions of the subspace are estimated directly
by iterating the estimating equations corresponding to the estimators. Second,
to reduce computation time even further five robust deterministic values are
proposed to initialize the algorithms instead of using random starting values.
It is shown that the new algorithms yield robust solutions and the computation
time is largely reduced, especially for high-dimensional data.

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The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
1
The categories of this paper are: cs
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn