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Fisher discriminant analysis with kernels
Abstract
A non-linear classification technique based on Fisher's discriminant is proposed. The main ingredient is the kernel trick which allows the efficient computation of Fisher discriminant in feature space. The linear classification in feature space corresponds to a (powerful) non-linear decision function in input space. Large scale simulations demonstrate the competitiveness of our approach.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
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Multi-Similarity Loss With General Pair Weighting for Deep Metric Learning
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Metadata
- Authors
- Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Mullers, K. R.
- Venue
- Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468)
- Publisher
- IEEE
- Published
- 2003-01-20
- Pages
- 41-48
- OpenAlex
- W2150796457
- Cited by
- 2,661
- References
- 1
- FWCI
- 202.22
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Mullers, K. R. (2003). Fisher discriminant analysis with kernels. Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468). https://doi.org/10.1109/nnsp.1999.788121
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