Multi-label dimensionality reduction and classification with extreme learning machines

查看全文 作　　者：Lin [1,2]Feng;Jing [1,2]Wang;Shenglan [1,2]Liu;Yao [1,2]Xiao

机构地区：[1]Faculty of Electronic Information and Electrical Engineering, School of Computer Science and Technology,Dalian University of Technology;[2]School of Innovation Experiment, Dalian University of Technology

出　　处：《Journal of Systems Engineering and Electronics》索引2014年第25卷第3期,共12页

基　　金：supported by the National Natural Science Foundation of China(51105052;61173163);the Liaoning Provincial Natural Science Foundation of China(201102037) 摘　　要：In the need of some real applications, such as text categorization and image classification, the multi-label learning gradually becomes a hot research point in recent years. Much attention has been paid to the research of multi-label classification algorithms. Considering the fact that the high dimensionality of the multi-label datasets may cause the curse of dimensionality and will hamper the classification process, a dimensionality reduction algorithm, named multi-label kernel discriminant analysis(MLKDA), is proposed to reduce the dimensionality of multi-label datasets. MLKDA, with the kernel trick, processes the multi-label integrally and realizes the nonlinear dimensionality reduction with the idea similar with linear discriminant analysis(LDA). In the classification process of multi-label data, the extreme learning machine(ELM) is an efficient algorithm in the premise of good accuracy. MLKDA, combined with ELM, shows a good performance in multi-label learning experiments with several datasets. The experiments on both static data and data stream show that MLKDA outperforms multi-label dimensionality reduction via dependence maximization(MDDM) and multi-label linear discriminant analysis(MLDA) in cases of balanced datasets and stronger correlation between tags, and ELM is also a good choice for multi-label classification. 关键词：降维算法图像分类学习机标签线性判别分析分类算法数据集文本分类

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Multi-label dimensionality reduction and classification with extreme learning machines

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