Multiview clustering in heterogeneous environment

Abstract

Integrating multiview cluster is a crucial issue in heterogeneous environment. In multiview clustering, objects from the multiple sources can be limited up to homogeneous environment by sharing the same dimensions. In this paper, novel-based tensor methods are used. They are (1) multiview clustering based on the integration of the Frobenius-norm objective function (MC-FR-OI) and (2) matrix integration in the Frobenius-norm objective function (MC-FR-MI). These frameworks worked by using tensor decomposition. Experimental results demonstrate that proposed methods are effective in multiview data integration. Here, higher-order data are used. The performance by using higher-order data is better when compared with the two-dimensional data.