Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Semi-supervised classification with cluster ensemble. / Berikov, Vladimir; Karaev, Nikita; Tewari, Ankit.
Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017. Institute of Electrical and Electronics Engineers Inc., 2017. стр. 245-250 8109880.Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Semi-supervised classification with cluster ensemble
AU - Berikov, Vladimir
AU - Karaev, Nikita
AU - Tewari, Ankit
PY - 2017/11/14
Y1 - 2017/11/14
N2 - We propose a method for semi-supervised classification using a combination of ensemble clustering and kernel based learning. The method works in two steps. In the first step, a number of variants of clustering partition are obtained with some clustering algorithm working on both labeled and unlabeled data. Weighted averaged co-association matrix is calculated using the results of partitioning. We prove that this matrix satisfies Mercer's condition, i.e., it defines symmetric non-negative definite kernel. In the second step, a decision function is constructed on labeled data using the obtained matrix as kernel. Some theoretical properties of the proposed method related to its convergence to the optimal classifier are investigated. Numerical experiments show that the proposed method possesses accuracy comparable with some state of the art methods, and in many cases outperforms them. We will illustrate the performance of the method on the problems of semi-supervised classification of hyperspectral images.
AB - We propose a method for semi-supervised classification using a combination of ensemble clustering and kernel based learning. The method works in two steps. In the first step, a number of variants of clustering partition are obtained with some clustering algorithm working on both labeled and unlabeled data. Weighted averaged co-association matrix is calculated using the results of partitioning. We prove that this matrix satisfies Mercer's condition, i.e., it defines symmetric non-negative definite kernel. In the second step, a decision function is constructed on labeled data using the obtained matrix as kernel. Some theoretical properties of the proposed method related to its convergence to the optimal classifier are investigated. Numerical experiments show that the proposed method possesses accuracy comparable with some state of the art methods, and in many cases outperforms them. We will illustrate the performance of the method on the problems of semi-supervised classification of hyperspectral images.
UR - http://www.scopus.com/inward/record.url?scp=85040514677&partnerID=8YFLogxK
U2 - 10.1109/SIBIRCON.2017.8109880
DO - 10.1109/SIBIRCON.2017.8109880
M3 - Conference contribution
AN - SCOPUS:85040514677
SP - 245
EP - 250
BT - Proceedings - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2017
Y2 - 18 September 2017 through 22 September 2017
ER -
ID: 9869985