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Graph classification based on sparse graph feature selection and extreme learning machine
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DOI：10.1016/j.neucom.2016.03.110.png)
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Abstract
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Identification and classification of graph data is a hot research issue in pattern recognition. The conventional methods of graph classification usually convert the graph data to the vector representation and then using SVM to be a classifier. These methods ignore the sparsity of graph data, and with the increase of the input sample, the storage and computation of the kernel matrix will cost a lot of memory and time. In this paper, we propose a new graph classification algorithm called graph classification based on sparse graph feature selection and extreme learning machine. The key of our method is using the lasso to select features because of the sparsity of graph data, and extreme learning machine (ELM) is introduced to the following classification task due to its good performance. Extensive experimental results on a series of benchmark graph data sets validate the effectiveness of the proposed methods. (C) 2017 Published by Elsevier B.V.
Keywords:
Graph kernel
Graph classification
Extreme learning machine
Lasso
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