Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical clustered multiclass discriminant analysis via
  cross-validation   Linear discriminant analysis (LDA) is a well-known method for multiclass
classification and dimensionality reduction. However, in general, ordinary LDA
does not achieve high prediction accuracy when observations in some classes are
difficult to be classified. This study proposes a novel cluster-based LDA
method that significantly improves the prediction accuracy. We adopt
hierarchical clustering, and the dissimilarity measure of two clusters is
defined by the cross-validation (CV) value. Therefore, clusters are constructed
such that the misclassification error rate is minimized. Our approach involves
a heavy computational load because the CV value must be computed at each step
of the hierarchical clustering algorithm. To address this issue, we develop a
regression formulation for LDA and construct an efficient algorithm that
computes an approximate value of the CV. The performance of the proposed method
is investigated by applying it to both artificial and real datasets. Our
proposed method provides high prediction accuracy with fast computation from
both numerical and theoretical viewpoints.

20
The categories of this paper are: acc-phys
1
The categories of this paper are: cs
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn