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Breaking the curse of dimensionality with Isolation Kernel
[article]
2021
arXiv
pre-print
The curse of dimensionality has been studied in different aspects. However, breaking the curse has been elusive. We show for the first time that it is possible to break the curse using the recently introduced Isolation Kernel. We show that only Isolation Kernel performs consistently well in indexed search, spectral & density peaks clustering, SVM classification and t-SNE visualization in both low and high dimensions, compared with distance, Gaussian and linear kernels. This is also supported by
arXiv:2109.14198v1
fatcat:hilkssihu5aevi6vp2mzvh7o3m