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Adaptively Weighted Multiview Proximity Learning for Clustering

聚类分析 关系(数据库) 计算机科学 相关性 人工智能 数据挖掘 机器学习 质量(理念) 模式识别(心理学) 数学 几何学 认识论 哲学
作者
Bao-Yu Liu,Ling Huang,Chang‐Dong Wang,Suohai Fan,Philip S. Yu
出处
期刊:IEEE transactions on cybernetics [Institute of Electrical and Electronics Engineers]
卷期号:51 (3): 1571-1585 被引量:23
标识
DOI:10.1109/tcyb.2019.2955388
摘要

Recently, the proximity-based methods have achieved great success for multiview clustering. Nevertheless, most existing proximity-based methods take the predefined proximity matrices as input and their performance relies heavily on the quality of the predefined proximity matrices. A few multiview proximity learning (MVPL) methods have been proposed to tackle this problem but there are still some limitations, such as only emphasizing the intraview relation but overlooking the inter-view correlation, or not taking the weight differences of different views into account when considering the inter-view correlation. These limitations affect the quality of the learned proximity matrices and therefore influence the clustering performance. With the aim of breaking through these limitations simultaneously, a novel proximity learning method, called adaptively weighted MVPL (AWMVPL), is proposed. In the proposed method, both the intraview relation and the inter-view correlation are considered. Besides, when considering the inter-view correlation, the weights of different views are learned in a self-weighted scheme. Furthermore, through an adaptively weighted scheme, the information of the learned view-specific proximity matrices is integrated into a view-common cluster indicator matrix which outputs the final clustering result. Extensive experiments are conducted on several synthetic and real-world datasets to demonstrate the effectiveness and superiority of our method compared with the existing methods.
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