Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds

The Journal of Machine Learning Research, Volume abs/1706.02803, Issue 12, 2019.

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Abstract:

Kernel k-means clustering can correctly identify and extract a far more varied collection of cluster structures than the linear k-means clustering algorithm. However, kernel k- means clustering is computationally expensive when the non-linear feature map is high-dimensional and there are many input points. Kernel approximation, e.g., the...More

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