Bibliographic Details
| Title: |
Hypergraph p-Laplacian Equations for Data Interpolation and Semi-supervised Learning. |
| Authors: |
Shi, Kehan1 (AUTHOR) kshi@cjlu.edu.cn, Burger, Martin2,3 (AUTHOR) |
| Source: |
Journal of Scientific Computing. Jun2025, Vol. 103 Issue 3, p1-17. 17p. |
| Abstract: |
Hypergraph learning with p-Laplacian regularization has attracted a lot of attention due to its flexibility in modeling higher-order relationships in data. This paper focuses on its fast numerical implementation, which is challenging due to the non-differentiability of the objective function and the non-uniqueness of the minimizer. We derive a hypergraph p-Laplacian equation from the subdifferential of the p-Laplacian regularization. A simplified equation that is mathematically well-posed and computationally efficient is proposed as an alternative. Numerical experiments verify that the simplified p-Laplacian equation suppresses spiky solutions in data interpolation and improves classification accuracy in semi-supervised learning. The remarkably low computational cost enables further applications. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
