Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Penalized Non-Linear Principal Components Analysis for Ordinal Variables
  with an Application to International Classification of Functioning Core Sets   Ordinal data occur frequently in the social sciences. When applying principal
components analysis (PCA), however, those data are often treated as numeric
implying linear relationships between the variables at hand, or non-linear PCA
is applied where the obtained quantifications are sometimes hard to interpret.
Non-linear PCA for categorical data, also called optimal scoring/scaling,
constructs new variables by assigning numerical values to categories such that
the proportion of variance in those new variables that is explained by a
predefined number of principal components is maximized. We propose a penalized
version of non-linear PCA for ordinal variables that is an intermediate between
standard PCAon category labels and non-linear PCA as used so far. The new
approach is by no means limited to monotonic effects, and offers both better
interpretability of the non-linear transformation of the category labels as
well as better performance on validation data than unpenalized non-linear PCA
and/or standard linear PCA. In particular, an application of penalized
non-linear PCA to ordinal data as given with the International Classification
of Functioning, Disability and Health (ICF) is provided.

10
The categories of this paper are: q-bio
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess