Multi-label dimensionality reduction and classification with extreme learning machines

• Published in: Journal of systems engineering and electronics 2014-06, Vol.25 (3), p.502-513
• Main Authors: Feng, Lin, Wang, Jing, Liu, Shenglan, Xiao, Yao
• Format: Article
• Language: English
• Subjects: Algorithms; Classification; Discriminant analysis; Elm; Kernels; Learning; Maximization; Neural networks; Reduction; 分类算法; 图像分类; 学习机; 数据集; 文本分类; 标签; 线性判别分析; 降维算法
• ISSN: 1004-4132; 1004-4132
• DOI: 10.1109/JSEE.2014.00058

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 wil 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.