Researchers have developed a new variant of TensorSketch, an algorithm used for efficient sketching of high-dimensional polynomial kernels. This improved version achieves a lower variance, scaling as 2^p/D compared to the previous 3^p/D, by utilizing complex random variables. The new method retains the input-sparsity running time advantage of the original TensorSketch algorithm while offering enhanced performance, as validated by experiments on synthetic and real-world datasets. AI
IMPACT This research offers a more efficient method for handling high-dimensional data, potentially improving performance in machine learning tasks that rely on kernel methods.
RANK_REASON The cluster describes a new variant of an existing algorithm with improved theoretical properties and experimental validation, fitting the definition of a research paper.
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- CountSketch
- Johnson–Lindenstrauss lemma
- Pham
- TensorSketch
- Wacker Chemie AG
- hsd17b12a
- Rameshwar Pratap
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