What is the primary purpose of the 'kernel trick' in spectral clustering?
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A
To speed up eigendecomposition of the similarity matrix
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B
To implicitly compute similarities in high-dimensional feature spaces without explicit mapping
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C
To normalize the graph Laplacian before clustering
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D
To select the optimal number of clusters automatically