Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
Classification at incomplete training information : Usage of group clustering to improve performance. / Berikov, Vladimir; Amirgaliyev, Yedilkhan; Cherikbayeva, Lyailya и др.
в: Journal of Theoretical and Applied Information Technology, Том 97, № 19, 01.01.2019, стр. 5048-5060.Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование
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TY - JOUR
T1 - Classification at incomplete training information
T2 - Usage of group clustering to improve performance
AU - Berikov, Vladimir
AU - Amirgaliyev, Yedilkhan
AU - Cherikbayeva, Lyailya
AU - Yedilkhan, Didar
AU - Tulegenova, Bakyt
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In this paper, we propose a method for semi-supervised classification based on a group solution to cluster analysis in combination with Laplacian regularization of similarity graph. The averaged co-association matrix obtained with the cluster ensemble is considered as a similarity matrix in the regularization context. We use a low-rank representation of the matrix that allows us to speed-up computations and save memory in the solution of the derived system of linear equations. Both theoretical studies and numerical experiments on artificial data and hyperspectral imagery confirm the efficiency of the method.
AB - In this paper, we propose a method for semi-supervised classification based on a group solution to cluster analysis in combination with Laplacian regularization of similarity graph. The averaged co-association matrix obtained with the cluster ensemble is considered as a similarity matrix in the regularization context. We use a low-rank representation of the matrix that allows us to speed-up computations and save memory in the solution of the derived system of linear equations. Both theoretical studies and numerical experiments on artificial data and hyperspectral imagery confirm the efficiency of the method.
KW - Cluster Ensemble
KW - Co-Association Matrix
KW - Low-Rank Representation
KW - Semi-Supervised Learning
UR - http://www.scopus.com/inward/record.url?scp=85074890934&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85074890934
VL - 97
SP - 5048
EP - 5060
JO - Journal of Theoretical and Applied Information Technology
JF - Journal of Theoretical and Applied Information Technology
SN - 1992-8645
IS - 19
ER -
ID: 22337963