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New Newton Algorithm Enhances Nonnegative Matrix Factorization with KL Divergence · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel Newton-type algorithm for Nonnegative Matrix Factorization (NMF) that utilizes the Kullback-Leibler (KL) divergence. This new method offers an efficient approach for analyzing count datasets, such as term-document matrices and images, by employing a second-order Taylor expansion of the loss function. The algorithm, which generalizes the HALS algorithm, has demonstrated provable convergence and competitive performance against existing state-of-the-art methods across various datasets. AI

IMPACT This research introduces a more efficient algorithm for NMF, potentially improving performance on count-based data analysis tasks in machine learning.

RANK_REASON The cluster contains a research paper detailing a new algorithm for a machine learning task.

Read on arXiv cs.LG →

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

New Newton Algorithm Enhances Nonnegative Matrix Factorization with KL Divergence · 2 sources tracked

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Damien Lesens, J\'er\'emy E. Cohen, Bora U\c{c}ar ·

    An Efficient Newton Algorithm for Nonnegative Matrix Factorization with the Kullback-Leibler Divergence

    arXiv:2607.13919v1 Announce Type: new Abstract: Nonnegative Matrix Factorization (NMF) is a fundamental tool in unsupervised learning, which approximates a nonnegative matrix by the product of two low-rank nonnegative factors. The Kullback-Leibler (KL) divergence is best suited t…

  2. arXiv cs.LG TIER_1 English(EN) · Bora Uçar ·

    An Efficient Newton Algorithm for Nonnegative Matrix Factorization with the Kullback-Leibler Divergence

    Nonnegative Matrix Factorization (NMF) is a fundamental tool in unsupervised learning, which approximates a nonnegative matrix by the product of two low-rank nonnegative factors. The Kullback-Leibler (KL) divergence is best suited to measure the data to model discrepancy when the…

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