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Semi-Supervised Dimensionality Reduction
Abstract
Dimensionality reduction is among the keys in mining high-dimensional data. This paper studies semi-supervised dimensionality reduction. In this setting, besides abundant unlabeled examples, domain knowledge in the form of pairwise constraints are available, which specifies whether a pair of instances belong to the same class (must-link constraints) or different classes (cannot-link constraints). We propose the SSDR algorithm, which can preserve the intrinsic structure of the unlabeled data as well as both the must-link and cannot-link constraints defined on the labeled examples in the projected low-dimensional space. The SSDR algorithm is efficient and has a closed form solution. Experiments on a broad range of data sets show that SSDR is superior to many established dimensionality reduction methods.
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Cited by
A Survey of Optimization Methods From a Machine Learning Perspective
Sun, S., Cao, Z., Zhu, H., & Zhao, J. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2019.2950779 · Cited by 789
Metadata
- Authors
- Zhang, D., Zhou, Z.-H., & Chen, S.
- Venue
- Proceedings of the 2007 SIAM International Conference on Data Mining
- Published
- 2007-04-26
- Pages
- 629-634
- OpenAlex
- W1564277727
- Cited by
- 313
- References
- 1
- FWCI
- 18.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhang, D., Zhou, Z.-H., & Chen, S. (2007). Semi-Supervised Dimensionality Reduction. Proceedings of the 2007 SIAM International Conference on Data Mining. https://doi.org/10.1137/1.9781611972771.73
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
