Robust graph learning from noisy data

• Main Authors: KANG, Zhao, PAN, Haiqi, HOI, Steven C. H., XU, Zenglin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Clustering; graph construction; noise removal; robust principle component analysis (RPCA); semisupervised classification; similarity measure; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5133; https://ink.library.smu.edu.sg/context/sis_research/article/6136/viewcontent/Robust_graph_learning_from_noisy_data_av.pdf
• Institution: Singapore Management University

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 r...