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- W2963456871 abstract "Kernel-based K-means clustering has gained popularity due to its simplicity and the power of its implicit non-linear representation of the data. A dominant concern is the memory requirement since memory scales as the square of the number of data points. We provide a new analysis of a class of approximate kernel methods that have more modest memory requirements, and propose a specific one-pass randomized kernel approximation followed by standard K-means on the transformed data. The analysis and experiments suggest the method is accurate, while requiring drastically less memory than standard kernel K-means and significantly less memory than Nystrom based approximations." @default.
- W2963456871 created "2019-07-30" @default.
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- W2963456871 date "2016-12-01" @default.
- W2963456871 modified "2023-09-26" @default.
- W2963456871 title "A randomized approach to efficient kernel clustering" @default.
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- W2963456871 doi "https://doi.org/10.1109/globalsip.2016.7905833" @default.
- W2963456871 hasPublicationYear "2016" @default.
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