Hypergraph Laplacian Eigenmaps and Face Recognition Problems
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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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 4 of its 17 references that are in the map. First posted on arXiv 27 May 2024.
Builds on
4 of 17 references.
References · 4
- 2020Directed hypergraph neural networkcited by 6
- 2018Hypergraph based semi-supervised learning algorithms applied to speech recognition problem: a novel approachcited by 3
- 2018Un-normalized hypergraph p-Laplacian based semi-supervised learning methodscited by 2
- 2019PageRank algorithm for Directed Hypergraphcited by 1
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