Fast kernel matrix computation for big data clustering

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20153401191949
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(1) Aristotle University of Thessaloniki, Thessaloniki, Greece | / Elsevier B.V., Netherlands期
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Kernel k-Means is a basis for many state of the art global clustering approaches. When the number of samples grows too big; however; it is extremely time-consuming to compute the entire kernel matrix and it is impossible to store it in the memory of a single computer. The algorithm of Approximate Kernel k-Means has been proposed; which works using only a small part of the kernel matrix. The computation of the kernel matrix; even a part of it; remains a significant bottleneck of the process. Some types of kernel; can be computed using matrix multiplication. Modern CPU architectures and computational optimization methods allow for very fast matrix multiplication; thus those types of kernel matrices can be computed much faster than others. © The Authors. Published by Elsevier B.V;
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