Robust Graph Learning from Noisy Data
This work addresses the challenge of noisy data in graph-based machine learning applications, offering improvements in domains like image and document analysis, but it appears incremental as it builds on existing robust PCA and manifold regularization techniques.
The paper tackles the problem of learning reliable graphs from noisy real-world data by proposing a robust graph learning scheme that adaptively removes noise and errors, resulting in significant performance boosts in clustering, semi-supervised classification, and data recovery tasks.
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.