  With the advance of the multi-media and multi-modal data, multi-view
clustering (MVC) has drawn increasing attentions recently. In this field, one
of the most crucial challenges is that the characteristics and qualities of
different views usually vary extensively. Therefore, it is essential for MVC
methods to find an effective approach that handles the diversity of multiple
views appropriately. To this end, a series of MVC methods focusing on how to
integrate the loss from each view have been proposed in the past few years.
Among these methods, the mainstream idea is assigning weights to each view and
then combining them linearly. In this paper, inspired by the effectiveness of
non-linear combination in instance learning and the auto-weighted approaches,
we propose Non-Linear Fusion for Self-Paced Multi-View Clustering (NSMVC),
which is totally different from the the conventional linear-weighting
algorithms. In NSMVC, we directly assign different exponents to different views
according to their qualities. By this way, the negative impact from the corrupt
views can be significantly reduced. Meanwhile, to address the non-convex issue
of the MVC model, we further define a novel regularizer-free modality of
Self-Paced Learning (SPL), which fits the proposed non-linear model perfectly.
Experimental results on various real-world data sets demonstrate the
effectiveness of the proposed method.
