EasyMKL

EasyMKL finds the kernels combination that maximizes the margin between classes. A relaxation of the maximum margin problem is considered to make the problem tractable. The computational complexity is comparable to a single SVM.

The algorithm learns a convex combination of base kernels with form

$$k_{\mu}(x,z)=\sum_r^P\mu_rk_r(x,z),\quad \mu_r\ge 0 \land\|\mu\|_1=1,$$

where $\mu$ is the weights vector learned by EasyMKL.

Paper

If you need additional details about EasyMKL, please refer to the following paper:
Fabio Aiolli and Michele Donini: "EasyMKL: a scalable multiple kernel learning algorithm". Neurocomputing (2015)

MKLpy.algorithms.EasyMKL(
    lam=.1, 
    learner=MKLpy.algorithms.KOMD(lam=.1), 
    solver='auto'
    **kwargs,
    )

Parameters (init)

Parameter	Type	Description
lam	double	a regularization hyper-parameter, with values in the range [0,1]. With lam=0, the algorithm maximizes the distance between classes, whereas with lam=1 EasyMKL maximizes the distance between centroids.
solver	str	solver used during the optimization, possible values are auto, libsvm, or cvxopt
**kwargs	args	MKL parameters, see here

Attributes

Attribute	Type	Description
n_kernels	int	number of combined kernels
KL	list	the training kernels list
func_form	callable	the combination function (e.g. summation, average...)
solution	dict	the solution of the optimization

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Methods

See standard MKL methods here

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Examples

from MKLpy.algorithms import EasyMKL
mkl = EasyMKL(lam=1.)
mkl = mkl.fit(KLtr, Ytr)

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Citing EasyMKL

If you use EasyMKL in your scientific project, please cite the following pubblication:

@article{aiolli2015easymkl,
  title={EasyMKL: a scalable multiple kernel learning algorithm},
  author={Aiolli, Fabio and Donini, Michele},
  journal={Neurocomputing},
  year={2015},
  publisher={Elsevier}
}