Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Compressed PCA Subspace Method for Anomaly Detection in
  High-Dimensional Data   Random projection is widely used as a method of dimension reduction. In
recent years, its combination with standard techniques of regression and
classification has been explored. Here we examine its use with principal
component analysis (PCA) and subspace detection methods. Specifically, we show
that, under appropriate conditions, with high probability the magnitude of the
residuals of a PCA analysis of randomly projected data behaves comparably to
that of the residuals of a similar PCA analysis of the original data. Our
results indicate the feasibility of applying subspace-based anomaly detection
algorithms to randomly projected data, when the data are high-dimensional but
have a covariance of an appropriately compressed nature. We illustrate in the
context of computer network traffic anomaly detection.

20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
1
The categories of this paper are: cs
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex