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Missiuro [ 21] had made an assumption that input argument (x i, x j ) is expressed into inner product (φ(x i ), φ(x j )) where kernel is a function that is defined as (8) K (x i, x j ) = [ φ (x i ), φ (x j ) ], where φ is kernel function that map input space into feature space.
The key is to apply a nonlinear mapping function to the input space and then evaluate it by the LRC in the higher dimensional feature space.
Let φ: R d → F be the mapping function from the input space to the feature space.
In this way, they can implement a proper non-linear function between the input space (sea state parameters space) and the output space (H s space).
PCA is performed in the original sample space, whereas kernel PCA (KPCA) applies kernel functions in the input space to achieve the same effect of the expensive nonlinear mapping.
Kernel functions perform nonlinear mapping between the input space and a feature space.
where φ is a nonlinear function which maps the input space into a higher dimensional space.
where φ is a nonlinear function which maps the input space into a higher-dimensional feature space.
This corresponds to a non-linear discriminant function in the original input space.
Besides the flexibility of the radial basis function (RBF) kernel function and its good generalization through the non-linear mapping of the input space to the infinite-dimensional feature space, the RBF kernel function produces SPD matrix.
Unlike the conventional fully connected feedforward multilayer neural networks for approximating functions on continuous input spaces, this paper investigates simplified neural networks (which use a common linear function in the hidden layer) for approximating functions on discrete input spaces.
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