期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 -, 期 -, 页码 -出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3201562
关键词
Representation learning; Adaptation models; Task analysis; Sun; Learning systems; Laplace equations; Correlation; Clustering; consensus representation learning; graph completion; multiview learning; nonnegative matrix factorization
类别
资金
- National Natural Science Foundation of China [62076096, 62006076]
- Shanghai Municipal Project [20511100900]
This paper introduces a novel Incomplete Multiview Nonnegative representation learning model (IMNGA) that simultaneously performs graph learning, missing graph completion, and consensus representation learning, effectively addressing the issue of modeling correlations in incomplete multiview clustering.
Despite incomplete multiview clustering (IMC) being widely studied in the past decade, it is still difficult to model the correlation among multiple views due to the absence of partial views. Most existing works for IMC only mine the correlation among multiple views from available views and ignore the importance of missing views. To address this issue, we propose a novel Incomplete Multiview Nonnegative representation learning model with Graph completion and Adaptive neighbors (IMNGA), which performs common graph learning, missing graph completion, and consensus nonnegative representation learning simultaneously. In IMNGA, the common graph on all views and the incomplete graph of each view are used to reconstruct the completed graph of the corresponding view, where the common graph satisfies the neighbor constraints of incomplete multiview data and consensus representation. IMNGA gets consensus representation by factorizing completed and incomplete graphs, where consensus representation satisfies the common graph constraint. IMNGA shows its effectiveness by outperforming other state-of-the-art methods.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据