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Learning with structured sparsity
Abstract
This paper investigates a new learning formulation called structured sparsity, which is a natural extension of the standard sparsity concept in statistical learning and compressive sensing. By allowing arbitrary structures on the feature set, this concept generalizes the group sparsity idea. A general theory is developed for learning with structured sparsity, based on the notion of coding complexity associated with the structure. Moreover, a structured greedy algorithm is proposed to efficiently solve the structured sparsity problem. Experiments demonstrate the advantage of structured sparsity over standard sparsity.
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Cited by
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Zhang, J., Wang, F.-Y., Wang, K., Lin, W.-H., Xu, X., & Chen, C. (2011) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2011.2158001 · Cited by 1,817
Optimization with Sparsity-Inducing Penalties
Bach, F., Jenatton, R., Mairal, J., & Obozinski, G. (2025) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000015 · Cited by 730
Traditional and recent approaches in background modeling for foreground detection: An overview
Bouwmans, T. (2014) · Computer Science Review · doi:10.1016/j.cosrev.2014.04.001 · Cited by 676
Sensor Technologies for Intelligent Transportation Systems
Guerrero-Ibáñez, J., Zeadally, S., & Contreras-Castillo, J. (2018) · Sensors · doi:10.3390/s18041212 · Cited by 637
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Bach, F. (2011) · now publishers, Inc. eBooks · doi:10.1561/9781601985118 · Cited by 608
Metadata
- Authors
- Huang, J., Zhang, T., & Metaxas, D.
- Venue
- Proceedings of the 26th Annual International Conference on Machine Learning
- Publisher
- ACM
- Published
- 2009-06-14
- Pages
- 417-424
- OpenAlex
- W2119330592
- Cited by
- 182
- References
- 0
- FWCI
- 59.08
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Huang, J., Zhang, T., & Metaxas, D. (2009). Learning with structured sparsity. Proceedings of the 26th Annual International Conference on Machine Learning. https://doi.org/10.1145/1553374.1553429
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