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New methods improve quadratic entropy approximation for large-scale analysis

By PulseAugur Editorial · [1 sources] ·

Researchers have developed efficient methods for approximating quadratic entropy, particularly for Euclidean and spherical geodesic distances. These techniques utilize random feature embeddings and projections to significantly reduce computational complexity. The approach is further optimized for large-scale computations with low memory and runtime requirements when the distance metric remains constant while the distribution varies. This method was demonstrated on Open Graph Benchmark datasets to analyze interdisciplinary reach based on citations and text features. AI

IMPACT These methods could enable more efficient analysis of large datasets in machine learning and bibliometrics.

RANK_REASON The cluster contains an academic paper detailing new computational methods for statistical analysis. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New methods improve quadratic entropy approximation for large-scale analysis

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The cluster contains an academic paper detailing new computational methods for statistical analysis. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Steve Huntsman ·

    Efficient quadratic entropy with distance sketches

    arXiv:2610.11976v1 Announce Type: cross Abstract: We detail scalable methods for approximating the quadratic entropy $p^T d p$ for arbitrary distributions $p$ and common distances $d$ of negative type. We focus on the Euclidean and spherical geodesic cases, which both use random …

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