School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Neural Netw. 2021 Aug;140:184-192. doi: 10.1016/j.neunet.2021.02.022. Epub 2021 Mar 6.
By utilizing the complementary information from multiple views, multi-view clustering (MVC) algorithms typically achieve much better clustering performance than conventional single-view methods. Although in this field, great progresses have been made in past few years, most existing multi-view clustering methods still suffer the following shortcomings: (1) most MVC methods are non-convex and thus are easily stuck into suboptimal local minima; (2) the effectiveness of these methods is sensitive to the existence of noises or outliers; and (3) the qualities of different features and views are usually ignored, which can also influence the clustering result. To address these issues, we propose dual self-paced multi-view clustering (DSMVC) in this paper. Specifically, DSMVC takes advantage of self-paced learning to tackle the non-convex issue. By applying a soft-weighting scheme of self-paced learning for instances, the negative impact caused by noises and outliers can be significantly reduced. Moreover, to alleviate the feature and view quality issues, we develop a novel feature selection approach in a self-paced manner and a weighting term for views. Experimental results on real-world data sets demonstrate the effectiveness of the proposed method.
利用来自多个视图的互补信息，多视图聚类（MVC）算法通常比传统的单视图方法实现更好的聚类性能。尽管在该领域中，近年来已经取得了很大的进展，但大多数现有的多视图聚类方法仍然存在以下缺点：（1）大多数 MVC 方法是非凸的，因此容易陷入次优的局部最小值；（2）这些方法的有效性对噪声或异常值的存在很敏感；（3）不同特征和视图的质量通常被忽略，这也会影响聚类结果。为了解决这些问题，我们在本文中提出了双自步多视图聚类（DSMVC）。具体来说，DSMVC 利用自步学习来解决非凸问题。通过对实例应用自步学习的软加权方案，可以显著降低噪声和异常值造成的负面影响。此外，为了缓解特征和视图质量问题，我们以自步的方式开发了一种新的特征选择方法和视图加权项。在真实数据集上的实验结果表明了所提出方法的有效性。
