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TensorSketch algorithm enhanced with complex random variables for improved performance

By PulseAugur Editorial · [2 sources] ·

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.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

TensorSketch algorithm enhanced with complex random variables for improved performance

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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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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Improving TensorSketch Using Complex Random Variables

    \texttt{TensorSketch} by~\cite{pham2013fast,kar2012random} provides efficient sketching algorithms for high-dimensional polynomial kernels $\vec{x}^{\otimes p} \in \R^{d^p}$. \cite{kar2012random} uses dense Johnson-Lindenstrauss (JL)-type projections with computational cost $O(pD…

  2. arXiv stat.ML TIER_1 English(EN) · Amit Sharma, Mohammad Azhar Khan, Rameshwar Pratap, Keegan Kang ·

    Improving TensorSketch Using Complex Random Variables

    arXiv:2608.10523v1 Announce Type: cross Abstract: \texttt{TensorSketch} by~\cite{pham2013fast,kar2012random} provides efficient sketching algorithms for high-dimensional polynomial kernels $\vec{x}^{\otimes p} \in \R^{d^p}$. \cite{kar2012random} uses dense Johnson-Lindenstrauss (…

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