Volume 32, Issue 4 e12215
RESEARCH ARTICLE
Free to Read

Comprehending international important Ramsar wetland documents using latent semantic topic model in kernel space

Ping Lin

Ping Lin

College of Electrical Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, China

Key Laboratory for Advanced Technology in Environmental Protection of Jiangsu Province, Yancheng Institute of Technology, Yancheng, Jiangsu, China

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Shanchao Jiang

Shanchao Jiang

College of Electrical Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, China

Key Laboratory for Advanced Technology in Environmental Protection of Jiangsu Province, Yancheng Institute of Technology, Yancheng, Jiangsu, China

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Du Li

Du Li

College of Electrical Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, China

Key Laboratory for Advanced Technology in Environmental Protection of Jiangsu Province, Yancheng Institute of Technology, Yancheng, Jiangsu, China

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Zhiyong Zou

Zhiyong Zou

College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, Sichuan, China

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Yongming Chen

Corresponding Author

Yongming Chen

College of Electrical Engineering, Yancheng Institute of Technology, Yancheng, Jiangsu, China

Key Laboratory for Advanced Technology in Environmental Protection of Jiangsu Province, Yancheng Institute of Technology, Yancheng, Jiangsu, China

Correspondence Yongming Chen, College of Electrical Engineering, Yancheng Institute of Technology, No.1 Middle Road Hope Avenue, Yancheng, 224051 Jiangsu, China. Email: billrange007@gmail.com

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First published: 25 February 2019
Citations: 1

Abstract

The kernel-based statistical semantic topic model is introduced for comprehending three species of internationally important Ramsar wetland documents describing the Lashi Lake wetland in the Yunnan Province, the Yancheng wetland in the Jiangsu Province, and the Zoige wetland in the Sichuan Province of China. Latent Dirichlet allocation (LDA) features are used to represent the semantic components of wetland documents. Kernel principal component analysis (KPCA) maps the topic components to the kernel space to attain the low dimensional principal components. Support vector machines (SVMs) are used to comprehend the semantic distribution of distinct wetland documents in the kernel space. The LDA+KPCA+SVM algorithm reaches 77.0% training and 75.9% test accuracy and 0.902 training and 0.840 test mean average precision scores in the application of comprehending the wetland documents, respectively. The performance of the proposed kernel-based model is superior to the traditional models of LDA+SVM and LDA+PCA+SVM.

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