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Most network-based machine learning methods assume that the labels of twoadjacent samples in the network are likely to be the same. However, assumingthe pairwise relationship between samples is not complete. The information agroup of samples that shows very similar pattern and tends to have similarlabels is missed. The natural way overcoming the information loss of the aboveassumption is to represent the feature dataset of samples as the hypergraph.Thus, in this paper, we will present the un-normalized hypergraph p-Laplaciansemi-supervised learning methods. These methods will be applied to the zoodataset and the tiny version of 20 newsgroups dataset. Experiment results showthat the accuracy performance measures of these un-normalized hypergraphp-Laplacian based semi-supervised learning methods are significantly greaterthan the accuracy performance measure of the un-normalized hypergraph Laplacianbased semi-supervised learning method (the current state of the art methodhypergraph Laplacian based semi-supervised learning method for classificationproblem with p=2).

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