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Communication Dans Un Congrès Année : 2011

Non-Negative Pre-Image in Machine Learning for Pattern Recognition

Résumé

Moreover, in order to have a physical interpretation, some constraints should be incorporated in the signal or image processing technique, such as the non-negativity of the solution. This paper deals with the non-negative pre-image problem in kernel machines, for nonlinear pattern recognition. While kernel machines operate in a feature space, associated to the used kernel function, a pre-image technique is often required to map back features into the input space. We derive a gradient-based algorithm to solve the pre-image problem, and to guarantee the non-negativity of the solution. Its convergence speed is significantly improved due to a weighted stepsize approach. The relevance of the proposed method is demonstrated with experiments on real datasets, where only a couple of iterations are necessary.
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Dates et versions

hal-01966029 , version 1 (27-12-2018)

Identifiants

  • HAL Id : hal-01966029 , version 1

Citer

Maya Kallas, Paul Honeine, Cédric Richard, Clovis Francis, Hassan Amoud. Non-Negative Pre-Image in Machine Learning for Pattern Recognition. Proc. 19th European Conference on Signal Processing (EUSIPCO), 2011, Barcelona, Spain. pp.931-935. ⟨hal-01966029⟩
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