1 research outputs found
ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering
Multi-view clustering has wide applications in many image processing
scenarios. In these scenarios, original image data often contain missing
instances and noises, which is ignored by most multi-view clustering methods.
However, missing instances may make these methods difficult to use directly and
noises will lead to unreliable clustering results. In this paper, we propose a
novel Auto-weighted Noisy and Incomplete Multi-view Clustering framework
(ANIMC) via a soft auto-weighted strategy and a doubly soft regular regression
model. Firstly, by designing adaptive semi-regularized nonnegative matrix
factorization (adaptive semi-RNMF), the soft auto-weighted strategy assigns a
proper weight to each view and adds a soft boundary to balance the influence of
noises and incompleteness. Secondly, by proposing{\theta}-norm, the doubly soft
regularized regression model adjusts the sparsity of our model by choosing
different{\theta}. Compared with existing methods, ANIMC has three unique
advantages: 1) it is a soft algorithm to adjust our framework in different
scenarios, thereby improving its generalization ability; 2) it automatically
learns a proper weight for each view, thereby reducing the influence of noises;
3) it performs doubly soft regularized regression that aligns the same
instances in different views, thereby decreasing the impact of missing
instances. Extensive experimental results demonstrate its superior advantages
over other state-of-the-art methods.Comment: Publisheded in IEEE Transactions on Artificial Intelligenc