Abstract
As an effective way of avoiding the curse of dimensionality and leveraging the predictive performance in high-dimensional regression analysis, dimension reduction suffers from small sample size. We proposed to utilize group information generated from pairwise data, to learn a low-dimensional representation highly correlated with target value. Experimental results on four public datasets imply that the proposed method can reduce regression error by effective dimension reduction.
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Notes
- 1.
Available in http://archive.ics.uci.edu/ml/datasets.html.
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Acknowledgements
This work was funded by Samsung Electronics Co., Ltd and the NSFC (Grant No. 61273299).
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Zhu, H., Shan, H., Lee, Y., He, Y., Zhou, Q., Zhang, J. (2016). Group Information-Based Dimensionality Reduction via Canonical Correlation Analysis. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9948. Springer, Cham. https://doi.org/10.1007/978-3-319-46672-9_34
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