Auto weighted robust dual graph nonnegative matrix factorization for multiview clustering

Abstract

Recent multi-data fusion techniques have inspired multi-view clustering study, which has become a popular topic in machine learning. One successful way of solving multi-view clustering is nonnegative matrix factorization. This paper proposes an auto weighted robust dual graph nonnegative matrix factorization (ARDNMF), where ℓ2,1 norm is adopted to make ARDNMF robust as data contain noise and outliers. Through dual graphs, structure information in data space and feature space is considered. Moreover, an auto weighted assignment method is introduced in ARDNMF to assign appropriate weights to views. Effective updating rules are given. Experiments on real datasets show ARDNMF superiors to state-of-the-art algorithms in terms of clustering results.