Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Feature Selection By KDDA For SVM-Based MultiView Face Recognition   Applications such as face recognition that deal with high-dimensional data
need a mapping technique that introduces representation of low-dimensional
features with enhanced discriminatory power and a proper classifier, able to
classify those complex features. Most of traditional Linear Discriminant
Analysis suffer from the disadvantage that their optimality criteria are not
directly related to the classification ability of the obtained feature
representation. Moreover, their classification accuracy is affected by the
"small sample size" problem which is often encountered in FR tasks. In this
short paper, we combine nonlinear kernel based mapping of data called KDDA with
Support Vector machine classifier to deal with both of the shortcomings in an
efficient and cost effective manner. The proposed here method is compared, in
terms of classification accuracy, to other commonly used FR methods on UMIST
face database. Results indicate that the performance of the proposed method is
overall superior to those of traditional FR approaches, such as the Eigenfaces,
Fisherfaces, and D-LDA methods and traditional linear classifiers.

21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin