/usr/include/shogun/statistics/MMDKernelSelectionCombOpt.h is in libshogun-dev 3.2.0-7.5.
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* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2012-2013 Heiko Strathmann
*/
#ifndef __MMDKERNELSELECTIONCOMBOPT_H_
#define __MMDKERNELSELECTIONCOMBOPT_H_
#include <shogun/statistics/MMDKernelSelectionComb.h>
namespace shogun
{
class CLinearTimeMMD;
/** @brief Implementation of optimal kernel selection for combined kernel.
* This class selects a combination of baseline kernels that maximises the
* ratio of the MMD and its standard deviation for a combined kernel. This
* boils down to solve the convex program
* \f[
* \min_\beta \{\beta^T (Q+\lambda_m) \beta \quad \text{s.t.}\quad \beta^T \eta=1, \beta\succeq 0\},
* \f]
* where \f$\eta\f$ is a vector whose elements are the MMDs of the baseline
* kernels and \f$Q\f$ is a linear time estimate of the covariance of \f$\eta\f$.
*
* This only works for the CLinearTimeMMD statistic. *
* IMPORTANT: The kernel has to be selected on different data than the two-sample
* test is performed on.
*
* The method is described in
* Gretton, A., Sriperumbudur, B., Sejdinovic, D., Strathmann, H.,
* Balakrishnan, S., Pontil, M., & Fukumizu, K. (2012).
* Optimal kernel choice for large-scale two-sample tests.
* Advances in Neural Information Processing Systems.
*/
class CMMDKernelSelectionCombOpt: public CMMDKernelSelectionComb
{
public:
/** Default constructor */
CMMDKernelSelectionCombOpt();
/** Constructor that initialises the underlying MMD instance
*
* @param mmd linear time mmd MMD instance to use.
* @param lambda ridge that is added to standard deviation, a sensible value
* is 10E-5 which is the default
*/
CMMDKernelSelectionCombOpt(CKernelTwoSampleTestStatistic* mmd,
float64_t lambda=10E-5);
/** Destructor */
virtual ~CMMDKernelSelectionCombOpt();
#ifdef HAVE_LAPACK
/** Computes optimal kernel weights using the ratio of the squared MMD by its
* standard deviation as a criterion, where both expressions are estimated
* in linear time.
*
* This boils down to solving a convex program which is quadratic in the
* number of kernels. See class description.
*
* SHOGUN has to be compiled with LAPACK to make this available. See
* set_opt* methods for optimization parameters.
*
* IMPORTANT: Kernel weights have to be learned on different data than is
* used for testing/evaluation!
*/
virtual SGVector<float64_t> compute_measures();
#endif
/** @return name of the SGSerializable */
const char* get_name() const { return "MMDKernelSelectionCombOpt"; }
private:
/** Initializer */
void init();
protected:
/** Ridge that is added to the diagonal of the Q matrix in the optimization
* problem */
float64_t m_lambda;
};
}
#endif /* __MMDKERNELSELECTIONCOMBOPT_H_ */
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