Learning graphs from data automatically has 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 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 PCA, 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, 2) improved graph construction by exploiting clean data recovered by robust PCA. Thus, it boosts the clustering, semi-supervised 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.

该论文提出了一种新颖的鲁棒图学习方案，能够通过自适应地消除原始数据中的噪声和错误来从真实世界的嘈杂数据中学习可靠的图，在数据聚类、半监督分类和数据恢复方面具有显著的性能提升，并在图像/文档聚类，目标识别，图像遮挡去除和视频背景减除等领域的大量实验中表现优于以前的最先进方法。

从嘈杂数据中学习鲁棒性图形