Abstract
Multi-label dimensionality reduction methods often ask for sufficient labeled samples and ignore abundant unlabeled ones. To leverage abundant unlabeled samples and scarce labeled ones, we introduce a method called Semi-supervised Multi-label Linear Discriminant Analysis (SMLDA). SMLDA measures the dependence between pairwise samples in the original space and in the projected subspace to utilize unlabeled samples. After that, it optimizes the target projective matrix by minimizing the distance of within-class samples, whilst maximizing the distance of between-class samples and the dependence term. Extensive empirical study on multi-label datasets shows that SMLDA outperforms other related methods across various evaluation metrics, and the dependence term is an effective alternative to the widely-used smoothness term.
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Acknowledgement
This work is supported by Natural Science Foundation of China (61402378), Natural Science Foundation of CQ CSTC (cstc2016jcyjA0351), Fundamental Research Funds for the Central Universities of China (XDJK2362015XK07 and XDJK2017D067).
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Yu, Y., Yu, G., Chen, X., Ren, Y. (2017). Semi-supervised Multi-label Linear Discriminant Analysis. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_71
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