Adaptive Weighted Multi-View Clustering

Shuo Shuo Liu, Lin Lin

Abstract:

Learning multi-view data is an emerging prob-
lem in machine learning research, and nonneg-
ative matrix factorization (NMF) is a popular
dimensionality-reduction method for integrat-
ing information from multiple views. These
views often provide not only consensus but also
complementary information. However, most
multi-view NMF algorithms assign equal weight
to each view or tune the weight via line search
empirically, which can be infeasible without any
prior knowledge of the views or computationally
expensive. In this paper, we propose a weighted
multi-view NMF (WM-NMF) algorithm. In
particular, we aim to address the critical tech-
nical gap, which is to learn both view-specific
weight and observation-specific reconstruction
weight to quantify each view’s information con-
tent. The introduced weighting scheme can al-
leviate unnecessary views’ adverse effects and
enlarge the positive effects of the important
views by assigning smaller and larger weights,
respectively. Experimental results confirm the
effectiveness and advantages of the proposed al-
gorithm in terms of achieving better clustering
performance and dealing with the noisy data
compared to the existing algorithms.