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New EventNMF model analyzes continuous-time event data directly

By PulseAugur Editorial · [2 sources] ·

Researchers have developed EventNMF, a novel continuous-time non-negative matrix factorization model designed to analyze event data directly. Unlike previous methods that require binning or smoothing, EventNMF operates on raw event times, preserving fine-grained temporal features and entity-level heterogeneities. The model uses a Poisson process framework with a non-negative B-spline basis to uncover shared temporal templates across entities, offering a mathematically sound, efficient, and easy-to-implement solution for applications in fields like neuroscience and social networks. AI

IMPACT Introduces a more precise method for analyzing temporal event data, potentially improving models in fields reliant on such data.

RANK_REASON The cluster contains an academic paper detailing a new method for analyzing event data.

Read on arXiv cs.LG →

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

New EventNMF model analyzes continuous-time event data directly

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Rapha\"el Romero ·

    Non-Negative Matrix Factorization for Event Data

    arXiv:2606.06205v1 Announce Type: new Abstract: Continuous-time event data, in which entities emit instantaneous events over time, arises naturally across many domains such as neuroscience, seismology, and social networks. Non-negative matrix factorization (NMF) is a natural tool…

  2. arXiv cs.LG TIER_1 English(EN) · Raphaël Romero ·

    Non-Negative Matrix Factorization for Event Data

    Continuous-time event data, in which entities emit instantaneous events over time, arises naturally across many domains such as neuroscience, seismology, and social networks. Non-negative matrix factorization (NMF) is a natural tool to uncover interpretable structure in such data…

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