Collective Matrix Completion via Graph Extraction

IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Tong Zhan;Xiaojun Mao;Jian Wang;Zhonglei Wang
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引用次数: 0

Abstract

Collective matrix completion (CMC) offers a straightforward approach to dealing with data with entries from various sources. Benefiting from the joint structure in the collective matrix, CMC often achieves fast convergence. However, since CMC conducts matrix-level operations, it neglects the entry-wise information that can potentially be very useful for matrix completion. In this paper, to capture the entry-wise information, we propose a method called graph collective matrix completion (GCoMC). Specifically, our method integrates a graph pattern extraction module into CMC via a relational graph convolutional network. Experiments on simulated and real-world datasets show that our method significantly outperforms some existing counterparts.
通过图形提取完成集合矩阵
集合矩阵补全(CMC)提供了一种处理来自不同来源条目的数据的直接方法。得益于集合矩阵中的联合结构,CMC 通常能实现快速收敛。然而,由于 CMC 进行的是矩阵级运算,它忽略了对矩阵补全可能非常有用的条目信息。在本文中,为了捕捉入口信息,我们提出了一种称为图集合矩阵完成(GCoMC)的方法。具体来说,我们的方法通过关系图卷积网络将图模式提取模块集成到 CMC 中。在模拟和实际数据集上的实验表明,我们的方法明显优于现有的一些同类方法。
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来源期刊
IEEE Signal Processing Letters
IEEE Signal Processing Letters 工程技术-工程:电子与电气
CiteScore
7.40
自引率
12.80%
发文量
339
审稿时长
2.8 months
期刊介绍: The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing. Papers published in the Letters can be presented within one year of their appearance in signal processing conferences such as ICASSP, GlobalSIP and ICIP, and also in several workshop organized by the Signal Processing Society.
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