Near Input Sparsity Time Kernel Embeddings via Adaptive Sampling

Amir Zandieh
Amir Zandieh
Cited by: 0|Bibtex|Views20|Links

Abstract:

To accelerate kernel methods, we propose a near input sparsity time algorithm for sampling the high-dimensional feature space implicitly defined by a kernel transformation. Our main contribution is an importance sampling method for subsampling the feature space of a degree $q$ tensoring of data points in almost input sparsity time, impr...More

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