Robust Graph Learning from Noisy Data

Abstract

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.

References in corpus (7)

• Robust Recovery of Subspace Structures by Low-Rank Representation
• Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization
• Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering
• Connections Between Nuclear Norm and Frobenius Norm Based Representations
• Low-rank Kernel Learning for Graph-based Clustering
• Robust PCA via Nonconvex Rank Approximation
• YoTube: Searching Action Proposal via Recurrent and Static Regression Networks

Cited by in corpus (23)

• Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings
• Pseudo-supervised Deep Subspace Clustering
• Self-supervised Consensus Representation Learning for Attributed Graph
• Large-scale Multi-view Subspace Clustering in Linear Time
• Multiple Partitions Aligned Clustering
• Sparse Graph Learning Under Laplacian-Related Constraints
• Structured Graph Learning for Scalable Subspace Clustering: From Single-view to Multi-view
• Towards Clustering-friendly Representations: Subspace Clustering via Graph Filtering
• Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
• Multilayer Collaborative Low-Rank Coding Network for Robust Deep Subspace Discovery
• Outlier Detection Ensemble with Embedded Feature Selection
• Scaled Simplex Representation for Subspace Clustering
• Multi-view Contrastive Graph Clustering
• Kernel Truncated Regression Representation for Robust Subspace Clustering
• Self-paced Principal Component Analysis
• Smoothed Multi-View Subspace Clustering
• Multi-graph Fusion for Multi-view Spectral Clustering
• Deep graph learning for semi-supervised classification
• RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity Preserved
• Multi-view Subspace Clustering via Partition Fusion
• Relation-Guided Representation Learning
• Structured Graph Learning for Clustering and Semi-supervised Classification
• Decentralized Learning of Tree-Structured Gaussian Graphical Models from Noisy Data