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The self-organizing map allows to visualize the underlying structure of high dimensional data. However, the original relies on the use of Euclidean distances which often becomes a serious drawback for number of real problems. Donald and others map the data in input space into a high 2-dimension feature space, here SOM algorithm are performed. However, its disadvantage lies in lack of direct descriptions...
Kernel Methods are algorithms that, by replacing the inner product with an appropriate positive definite function, implicitly perform a nonlinear mapping of the input data into a high-dimensional feature space. In this paper, a novel kernel SOM (seIf-organizing map) algorithm is proposed based on energy function for solving the disadvantage lies in lack of direct descriptions about the clusterings'...
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