Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Which principal components are most sensitive to distributional changes?   PCA is often used in anomaly detection and statistical process control tasks.
For bivariate data, we prove that the minor projection (the least varying
projection) of the PCA-rotated data is the most sensitive to distributional
changes, where sensitivity is defined by the Hellinger distance between
distributions before and after a change. In particular, this is almost always
the case if only one parameter of the bivariate normal distribution changes,
i.e., the change is sparse. Simulations indicate that the minor projections are
the most sensitive for a large range of changes and pre-change settings in
higher dimensions as well. This motivates using the minor projections for
detecting sparse distributional changes in high-dimensional data.

22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
1
The categories of this paper are: cs
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th