Description

Abstract

Clustering by integrating multi-view representations has become a crucial issue for

knowledge discovery in heterogeneous environments. However, most prior approaches

assume that the multiple representations share the same dimension, limiting their

applicability to homogeneous environments.

In this a novel tensor-based framework for integrating heterogeneous multi-view data in the

context of spectral clustering. Our framework includes two novel formulations, that is multi-

view clustering based on the integration of the Frobenius-norm objective function (MC-FR-

OI) and that based on matrix integration in the Frobenius-norm objective function (MC-FR-

MI). It shows that the solutions for both formulations can be computed by tensor

decompositions. It evaluated our methods on synthetic data and two real-world data sets in

comparison with baseline methods. Experimental results demonstrate that the proposed

formulations are effective in integrating multi-view data in heterogeneous environments.