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 -