# Hypergraph Laplacian Eigenmaps and Face Recognition Problems

arXiv: https://arxiv.org/abs/2405.16748 · HTML: https://mlsanity.com/paper/2405.16748/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 4 of 4)

- [Directed hypergraph neural network](https://mlsanity.com/paper/2008.03626/) (2020)
- [Hypergraph based semi-supervised learning algorithms applied to speech recognition problem: a novel approach](https://mlsanity.com/paper/1810.12743/) (2018)
- [Un-normalized hypergraph p-Laplacian based semi-supervised learning methods](https://mlsanity.com/paper/1811.02986/) (2018)
- [PageRank algorithm for Directed Hypergraph](https://mlsanity.com/paper/1909.01132/) (2019)

## Built on by (most cited first, 1 of 1)

- [Compute the edge p-Laplacian centrality for air traffic network](https://mlsanity.com/paper/2512.14749/) (2025)

## Abstract (arXiv)

Face recognition is a very important topic in data science and biometric security research areas. It has multiple applications in military, finance, and retail, to name a few. In this paper, the novel hypergraph Laplacian Eigenmaps will be proposed and combine with the k nearest-neighbor method and/or with the kernel ridge regression method to solve the face recognition problem. Experimental results illustrate that the accuracy of the combination of the novel hypergraph Laplacian Eigenmaps and one specific classification system is similar to the accuracy of the combination of the old symmetric normalized hypergraph Laplacian Eigenmaps method and one specific classification system.