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Multiclass multiple kernel learning
Open accessAbstract
In many applications it is desirable to learn from several kernels. "Multiple kernel learning" (MKL) allows the practitioner to optimize over linear combinations of kernels. By enforcing sparse coefficients, it also generalizes feature selection to kernel selection. We propose MKL for joint feature maps. This provides a convenient and principled way for MKL with multiclass problems. In addition, we can exploit the joint feature map to learn kernels on output spaces. We show the equivalence of several different primal formulations including different regularizers. We present several optimization methods, and compare a convex quadratically constrained quadratic program (QCQP) and two semi-infinite linear programs (SILPs) on toy data, showing that the SILPs are faster than the QCQP. We then demonstrate the utility of our method by applying the SILP to three real world datasets.
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Cites
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
Multiple kernel learning, conic duality, and the SMO algorithm
Bach, F. R., Lanckriet, G. R. G., & Jordan, M. I. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015424 · Cited by 1,453
Cited by
On feature combination for multiclass object classification
Gehler, P., & Nowozin, S. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459169 · Cited by 797
Criminisi, A., Shotton, J., & Konukoglu, E. (2012) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000035 · Cited by 677
Morphological and molecular breast cancer profiling through explainable machine learning
Binder, A., Bockmayr, M., Hägele, M., Wienert, S., Heim, D., Hellweg, K., Ishii, M., Stenzinger, A., Hocke, A., Denkert, C., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00303-4 · Cited by 156
Incomplete multi-modal representation learning for Alzheimer’s disease diagnosis
Liu, Y., Fan, L., Zhang, C., Zhou, T., Xiao, Z., Geng, L., & Shen, D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101953 · Cited by 102
Metadata
- Authors
- Zien, A., & Ong, C. S.
- Venue
- Proceedings of the 24th international conference on Machine learning
- Publisher
- ACM
- Published
- 2007-06-20
- Pages
- 1191-1198
- OpenAlex
- W2106503792
- Cited by
- 266
- References
- 2
- FWCI
- 13.66
- Type
- conference paper
- Language
- en
- Open access
- green
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