Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2020

Abstract

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.

Keywords

Clustering, graph construction, noise removal, robust principle component analysis (RPCA), semisupervised classification, similarity measure

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Cybernetics

Volume

50

Issue

5

First Page

1833

Last Page

1843

ISSN

2168-2267

Identifier

10.1109/TCYB.2018.2887094

Publisher

IEEE

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TCYB.2018.2887094

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