兼顾显隐信息与特征加权的多视角模糊聚类

doi:10.3778/j.issn.1673-9418.2005015

摘要/Abstract

摘要：

多视角聚类是一类应用于无监督学习的多视角学习方法，其旨在利用不同视角的特征集去提升聚类的效果。虽然目前多视角聚类已经在很多领域取得了有效的应用，但仍然面临很多挑战，例如传统的算法仅仅利用显性信息进行聚类，忽视了隐性信息的重要性。提出一种兼顾显隐信息与特征加权的多视角模糊聚类算法（MVSH），该算法在模糊聚类框架下实现各视角的协同学习。一方面，通过为每个视角下的特征加权进行聚类，得到个性化信息；另一方面，以特征学习的方式抽取多视角数据集共享的系数矩阵，得到共性（隐）信息，实现了一种显隐视角协同的多视角学习。使用显信息和隐信息可以在多视角聚类协同学习的过程中较好地平衡视角间共性信息和个性化信息。在多视角数据集的实验研究也有效验证了MVSH的上述优点。和多个相关算法的性能比较表明该方法能得到更好或至少相当的性能。通过在多个多视角数据集上的实验证明，提出的融合显隐信息的策略比单独使用显、隐信息时有更好的效果。

关键词: 隐信息, 特征加权, 多视角学习, 协同学习

Abstract:

Multi-view clustering is a type of multi-view learning method applied to unsupervised learning, which aims to use the feature set of different views to enhance the effect of clustering. Although multi-view clustering has been effectively applied in many fields, it still faces many challenges. For example, traditional algorithms only use visual information for clustering, ignoring the importance of hidden information. This paper proposes a multi-view fuzzy clustering algorithm (MVSH) that considers both visual and hidden information and feature weighting. This algorithm implements collaborative learning from various views under the framework of fuzzy clustering. On the one hand, personalized information is obtained by clustering features weighted under each view. On the other hand, the coefficient matrix shared by multi-view data sets is extracted by feature learning to obtain common (hidden) information. A multi-view learning with visual and hidden collaboration is realized. Using visual information and hidden information can better balance the common information and personalized information in the process of multi-view clustering collaborative learning. Experimental studies on multi-view data sets also effectively verify the above advantages of MVSH. Comparison with the performance of multiple related algorithms shows that the proposed method can achieve better or at least comparable performance. Experiments on multiple multi-view data sets prove that the strategy of merging visual and hidden information proposed in this paper has better effect than using visual and hidden information alone.

Key words: hidden information, feature weighting, multi-view learning, collaborative learning

梁凌, 邓赵红, 王士同. 兼顾显隐信息与特征加权的多视角模糊聚类[J]. 计算机科学与探索, 2021, 15(6): 1092-1102.

LIANG Ling, DENG Zhaohong, WANG Shitong. Multi-view Fuzzy Clustering Combining Visual and Hidden Information with Feature Weighting[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(6): 1092-1102.

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