Classification at incomplete training information: Usage of group clustering to improve performance

Vladimir Berikov, Yedilkhan Amirgaliyev, Lyailya Cherikbayeva, Didar Yedilkhan, Bakyt Tulegenova

Результат исследования: Научные публикации в периодических изданияхстатья

1 Цитирования (Scopus)

Аннотация

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.

Язык оригиналаанглийский
Страницы (с-по)5048-5060
Число страниц13
ЖурналJournal of Theoretical and Applied Information Technology
Том97
Номер выпуска19
СостояниеОпубликовано - 1 янв 2019

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