  Exploiting different representations, or views, of the same object for better
clustering has become very popular these days, which is conventionally called
multi-view clustering. Generally, it is essential to measure the importance of
each individual view, due to some noises, or inherent capacities in
description. Many previous works model the view importance as weight, which is
simple but effective empirically. In this paper, instead of following the
traditional thoughts, we propose a new weight learning paradigm in context of
multi-view clustering in virtue of the idea of re-weighted approach, and we
theoretically analyze its working mechanism. Meanwhile, as a carefully achieved
example, all of the views are connected by exploring a unified Laplacian rank
constrained graph, which will be a representative method to compare with other
weight learning approaches in experiments. Furthermore, the proposed weight
learning strategy is much suitable for multi-view data, and it can be naturally
integrated with many existing clustering learners. According to the numerical
experiments, the proposed intrinsic weight learning approach is proved
effective and practical to use in multi-view clustering.
