Robust Graph Learning from Noisy Data

2 code implementations • 17 Dec 2018 • Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu

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.

Clustering General Classification +7