TY  - JOUR
TI  - The nearest polyhedral convex conic regions for high-dimensional classification
AB  - In the nearest-convex-model type classifiers, each class in the training set is approximated with a convex
class model, and a test sample is assigned to a class based on the shortest distance from the test sample to these class
models. In this paper, we propose new methods for approximating the distances from test samples to the convex regions
spanned by training samples of classes. To this end, we approximate each class region with a polyhedral convex conic
region by utilizing polyhedral conic functions (PCFs) and its extension, extended PCFs. Then, we derive the necessary
formulations for computing the distances from test samples to these new models. We tested the proposed methods
on different high-dimensional classification tasks including face, digit, and generic object classification as well as on
some lower-dimensional classification problems. The experimental results on different datasets show that the proposed
classifiers achieve either the best or comparable results on high-dimensional classification problems compared to other
nearest-convex-model classifiers, which shows the superiority of the proposed methods
AU  - Cimen, Emre
AU  - Ozturk, Gurkan
AU  - Cevikalp, Hakan
DO  - 10.3906/elk-2005-142
PY  - 2021
JO  - Turkish Journal of Electrical Engineering and Computer Sciences
VL  - 29
IS  - 2
SN  - 1300-0632
SP  - 913
EP  - 928
DB  - TRDizin
UR  - http://search/yayin/detay/514948
ER  -