Multimodal Feature Fusion Based Hypergraph Learning Model

Table 4

Time cost comparison.

Datasets	Model	Accuracy	Precision	Recall	F1	Time (s)
Cat & Dog	RVGG	0.965	0.965	0.965	0.965	20.26
	RVGG + HVGG	0.975	0.975	0.977	0.976	49.04
	Incidence matrix extension
	RVGG + HVGG	0.986	0.986	0.986	0.986	27.35
	Incidence matrix extension + Laplacian matrix fusion
Cifar 10	RVGG	0.561	0.570	0.564	0.567	11236
	RVGG + HVGG	0.564	0.573	0.573	0.573	23793
	Incidence matrix extension
	RVGG + HVGG	0.594	0.613	0.583	0.598	13158
	Incidence matrix extension + Laplacian matrix fusion
Ctrip	Doc2vec	0.651	0.870	0.657	0.748	59.7
	Doc2vec + word2vec	0.659	0.878	0.709	0.785	106.86
	Incidence matrix extension
	Doc2vec + word2vec	0.663	0.884	0.721	0.851	74.2
	Incidence matrix extension + Laplacian matrix fusion
Spambase	Euclidean	0.646	0.701	0.646	0.672	8.17
	Cosin + Euclidean incidence matrix extension	0.654	0.729	0.654	0.689	17.3
	Cosin + Euclidean incidence matrix extension + Laplacian matrix fusion	0.712	0.734	0.696	0.714	10.25