ℓp norm multiple kernel Fisher discriminant analysis for object and image categorisation

@article{Yan2010pNM,
  title={ℓp norm multiple kernel Fisher discriminant analysis for object and image categorisation},
  author={Fei Yan and Krystian Mikolajczyk and Mark Barnard and Hongping Cai and Josef Kittler},
  journal={2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
  year={2010},
  pages={3626-3632}
}
In this paper, we generalise multiple kernel Fisher discriminant analysis (MK-FDA) such that the kernel weights can be regularised with an ℓp norm for any p ≥ 1, in contrast to existing MK-FDA that uses either l1 or l2 norm. We present formulations for both binary and multiclass cases and solve the associated optimisation problems efficiently with semi-infinite programming. We show on three object and image categorisation benchmarks that by learning the intrinsic sparsity of a given set of base… CONTINUE READING
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