Open AccessComprehending international important Ramsar wetland documents using latent semantic topic model in kernel space
Author(s) -
Lin Ping,
Jiang Shanchao,
Li Du,
Zou Zhiyong,
Chen Yongming
Publication year - 2019
Publication title -
natural resource modeling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.28
H-Index - 32
eISSN - 1939-7445
pISSN - 0890-8575
DOI - 10.1111/nrm.12215
Subject(s) - latent dirichlet allocation , kernel principal component analysis , kernel (algebra) , computer science , artificial intelligence , support vector machine , wetland , principal component analysis , pattern recognition (psychology) , latent semantic analysis , topic model , mathematics , kernel method , ecology , biology , combinatorics
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.