Robust Graph Learning From Noisy Data
Robust Graph Learning From Noisy Data
复制标题
从噪声数据中进行鲁棒图学习
DOI：
10.1109/tcyb.2018.2887094
复制
发表时间：
2020-05-01
影响因子：
11.8
通讯作者：
Xu, Zenglin
中科院分区：
文献类型：
--
作者：
Kang, Zhao;Pan, Haiqi;Xu, Zenglin
从数据中自动学习图形在聚类和半监督学习任务上显示出令人鼓舞的性能。然而，实际数据经常被破坏，这可能导致学习图不准确或不可靠。在本文中，我们提出了一种新的鲁棒图学习方案，通过自适应地去除原始数据中的噪声和误差，从现实世界的噪声数据中学习可靠的图。我们表明，我们提出的模型也可以被视为流形正则鲁棒主成分分析（RPCA）的鲁棒版本，其中图的质量起着关键作用。该模型能够显著提高数据聚类、半监督分类和数据恢复的性能，主要得益于两个关键因素：1)利用图平滑假设增强低秩恢复；2)利用RPCA恢复的干净数据改进图构建。因此，它总体上提高了集群、半监督分类和数据恢复性能。在图像/文档聚类、对象识别、图像阴影去除和视频背景减除方面的大量实验表明，我们的模型优于以前最先进的方法。
Learning graphs from data automatically have shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreliable. In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from the real-world noisy data by adaptively removing noise and errors in the raw data. We show that our proposed model can also be viewed as a robust version of manifold regularized robust principle component analysis (RPCA), where the quality of the graph plays a critical role. The proposed model is able to boost the performance of data clustering, semisupervised classification, and data recovery significantly, primarily due to two key factors: 1) enhanced low-rank recovery by exploiting the graph smoothness assumption and 2) improved graph construction by exploiting clean data recovered by RPCA. Thus, it boosts the clustering, semisupervised classification, and data recovery performance overall. Extensive experiments on image/document clustering, object recognition, image shadow removal, and video background subtraction reveal that our model outperforms the previous state-of-the-art methods.