Abstract
High-dimensional data are commonly met in multi-label learning, and dimensionality reduction is an important and challenging work. In this paper, we propose sparse matrix feature selection to reduce data dimension in multi-label learning. First, the feature selection problem is formalized by sparse matrix. Second, an sparse matrix feature selection algorithm is proposed. Third, four feature selection are compared with the proposed methods and parameter optimization analysis is also provide. Experiments reported the proposed algorithms outperform the other methods in most cases of tested datasets.
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Acknowledgments
This work is in part supported by National Nature Science Foundation of China under Grant Nos. 61170128 and 61379049, the Key Project of Education Department of Fujian Province under Grant No. JA13192, the Zhangzhou Municipal Natural Science Foundation under Grant No. ZZ2013J03, and the Minnan Normal University Doctoral Research Foundation under Grant No. 2004L21424.
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Yang, W., Zhou, B., Zhu, W. (2015). Sparse Matrix Feature Selection in Multi-label Learning. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_30
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DOI: https://doi.org/10.1007/978-3-319-25783-9_30
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