Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reduction of Feature Vectors Using Rough Set Theory for Human Face
  Recognition   In this paper we describe a procedure to reduce the size of the input feature
vector. A complex pattern recognition problem like face recognition involves
huge dimension of input feature vector. To reduce that dimension here we have
used eigenspace projection (also called as Principal Component Analysis), which
is basically transformation of space. To reduce further we have applied feature
selection method to select indispensable features, which will remain in the
final feature vectors. Features those are not selected are removed from the
final feature vector considering them as redundant or superfluous. For
selection of features we have used the concept of reduct and core from rough
set theory. This method has shown very good performance. It is worth to mention
that in some cases the recognition rate increases with the decrease in the
feature vector dimension.

13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
22
The categories of this paper are: chao-dyn