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This paper proposes a novel framework for accelerating support vector
clustering. The proposed method first computes much smaller compressed data
sets while preserving the key cluster properties of the original data sets
based on a novel spectral data compression approach. Then, the resultant
spectrally-compressed data sets are leveraged for the development of fast and
high quality algorithm for support vector clustering. We conducted extensive
experiments using real-world data sets and obtained very promising results. The
proposed method allows us to achieve 100X and 115X speedups over the state of
the art SVC method on the Pendigits and USPS data sets, respectively, while
achieving even better clustering quality. To the best of our knowledge, this
represents the first practical method for high-quality and fast SVC on
large-scale real-world data sets

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