Abstract
In multi-label learning, each training example is represented by an instance while associated with multiple class labels simultaneously. Most existing approaches make use of multi-label training examples by utilizing the logical labeling information, i.e., one class label is either fully relevant or irrelevant to the instance. In this paper, a novel multi-label learning approach is proposed which aims to enhance the labeling information by extending logical labels into numerical labels. Firstly, a stacked matrix is constructed where the feature and the logical label matrix are placed vertically. Secondly, the labeling information is enhanced by leveraging the underlying low-rank structure in the stacked matrix. Thirdly, the multi-label predictive model is induced by the learning procedure from training examples with numerical labels. Extensive comparative studies clearly validate the advantage of the proposed method against the state-of-the-art multi-label learning approaches.
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The data sets can be downloaded from: http://meka.sourceforge.net/#datasets and http://mulan.sourceforge.net/datasets.html.
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Acknowledgements
This research was supported by the National Key Research & Development Plan of China (No. 2017YFB1002801), the National Science Foundation of China (61622203), the Jiangsu Natural Science Funds for Distinguished Young Scholar (BK20140022), the Collaborative Innovation Center of Novel Software Technology and Industrialization, and the Collaborative Innovation Center of Wireless Communications Technology.
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Tao, A., Xu, N., Geng, X. (2018). Labeling Information Enhancement for Multi-label Learning with Low-Rank Subspace. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_51
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