Skip to Main content Skip to Navigation
New interface
Journal articles

Consistent Semi-Supervised Graph Regularization for High Dimensional Data

Abstract : Semi-supervised Laplacian regularization, a standard graph-based approach for learning from both labelled and unlabelled data, was recently demonstrated to have an insignificant high dimensional learning efficiency with respect to unlabelled data (Mai and Couillet, 2018), causing it to be outperformed by its unsupervised counterpart, spectral clustering, given sufficient unlabelled data. Following a detailed discussion on the origin of this inconsistency problem, a novel regularization approach involving centering operation is proposed as solution, supported by both theoretical analysis and empirical results.
Document type :
Journal articles
Complete list of metadata
Contributor : DELPHINE LE PIOLET Connect in order to contact the contributor
Submitted on : Tuesday, June 7, 2022 - 4:25:12 PM
Last modification on : Friday, July 1, 2022 - 4:46:50 PM


Distributed under a Creative Commons Attribution 4.0 International License

Links full text


  • HAL Id : hal-03689888, version 1
  • ARXIV : 2006.07575


Xiaoyi Mai, Romain Couillet. Consistent Semi-Supervised Graph Regularization for High Dimensional Data. Journal of Machine Learning Research, 2021, 22, pp.1-48. ⟨hal-03689888⟩



Record views