# Consistent Semi-Supervised Graph Regularization for High Dimensional Data

arXiv: https://arxiv.org/abs/2006.07575 · HTML: https://mlsanity.com/paper/2006.07575/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [GraphHop: An Enhanced Label Propagation Method for Node Classification](https://mlsanity.com/paper/2101.02326/) (2021)
- [A Random Matrix Perspective on Random Tensors](https://mlsanity.com/paper/2108.00774/) (2021)
- [Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption](https://mlsanity.com/paper/2310.13434/) (2023)
- [Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture](https://mlsanity.com/paper/2303.02048/) (2023)
- [The Breakdown of Gaussian Universality in Classification of High-dimensional Linear Factor Mixtures](https://mlsanity.com/paper/2410.05609/) (2024)
- [Label Efficient Regularization and Propagation for Graph Node Classification](https://mlsanity.com/paper/2204.08646/) (2022)
- [Consistency of semi-supervised learning, stochastic tug-of-war games, and the p-Laplacian](https://mlsanity.com/paper/2401.07463/) (2024)
- [A Large Dimensional Analysis of Multi-task Semi-Supervised Learning](https://mlsanity.com/paper/2402.13646/) (2024)
- [High-dimensional Semi-supervised Classification via the Fermat Distance](https://mlsanity.com/paper/2604.23573/) (2026)
- [Asymptotic Bayes risk of semi-supervised learning with uncertain labeling](https://mlsanity.com/paper/2403.17767/) (2024)

## Abstract (arXiv)

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.