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A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression
We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Using hierarchical matrix approximations for the kernel matrix the memory requirements, the number of floating point operations, and the execution time are drastically reduced compared to standard dense
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