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In kernel methods, it can be obtained by using kernel trick [21].
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Later on, the nonlinear extension of the method using kernel trick was presented in [48].
We use kernel trick to solve this problem.
This mapping is done by using kernel function such as polynomial or radial basis function kernel.
The explicit knowledge of the nonlinear mapping function can be avoided by using the kernel trick.
Fortunately, this explicit knowledge of the nonlinear mapping function can be computationally avoided by using the kernel trick [40].
They present many similarities, for example its potential to work in nonlinear feature spaces by using the kernel trick.
We can try this in the original space or we can map the points to another space by using the kernel trick.
Using kernel methods makes it possible to represent the original data by using a matrix representation, called a kernel matrix.
The paper authored by W. Liu and J. C. Príncipe presents a general framework for using the "kernel trick" in the context of affine projection algorithms, as well as some procedures resulting from this framework.
Especially for low inter- and high intra-class variances, as generally expected for in-situ plankton images, it is recommended to apply methods for mapping input features into higher dimensional spaces, using the kernel trick (e.g. Support Vector Machines).
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