Un-normalized hypergraph p-Laplacian based semi-supervised learning methods

TL;DR

This paper introduces un-normalized hypergraph p-Laplacian semi-supervised learning methods that better capture group relationships in data, outperforming existing hypergraph Laplacian approaches in classification accuracy.

Contribution

The paper presents a novel un-normalized hypergraph p-Laplacian semi-supervised learning framework that enhances label propagation by modeling higher-order relationships.

Findings

Significantly higher accuracy than hypergraph Laplacian methods

Effective on datasets like zoo and 20 newsgroups

Demonstrates the advantage of hypergraph p-Laplacian in semi-supervised learning

Abstract

Most network-based machine learning methods assume that the labels of two adjacent samples in the network are likely to be the same. However, assuming the pairwise relationship between samples is not complete. The information a group of samples that shows very similar pattern and tends to have similar labels is missed. The natural way overcoming the information loss of the above assumption is to represent the feature dataset of samples as the hypergraph. Thus, in this paper, we will present the un-normalized hypergraph p-Laplacian semi-supervised learning methods. These methods will be applied to the zoo dataset and the tiny version of 20 newsgroups dataset. Experiment results show that the accuracy performance measures of these un-normalized hypergraph p-Laplacian based semi-supervised learning methods are significantly greater than the accuracy performance measure of the un-normalized hypergraph Laplacian based semi-supervised learning method (the current state of the art method hypergraph Laplacian based semi-supervised learning method for classification problem with p=2).