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New CP-GAMP algorithm accelerates Bayesian tensor reconstruction

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm called CP generalized approximate message passing (CP-GAMP) for Bayesian CANDECOMP/PARAFAC (CP) decomposition. This method significantly reduces computation time compared to traditional variational Bayesian methods by employing Gaussian message approximations to bypass high-dimensional matrix inversions. The algorithm also incorporates a Bernoulli-Gaussian prior with expectation-maximization updates for estimating CP rank and noise variance, and its performance is validated through synthetic and image-inpainting experiments. AI

IMPACT This new algorithm could improve the efficiency of tensor reconstruction tasks in machine learning.

RANK_REASON The cluster contains a research paper detailing a new algorithm for tensor reconstruction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New CP-GAMP algorithm accelerates Bayesian tensor reconstruction

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The cluster contains a research paper detailing a new algorithm for tensor reconstruction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Bingyang Cheng, Zhongtao Chen, Yichen Jin, Hao Zhang, Chen Zhang, Edmund Y. Lam, Yik-Chung Wu ·

    Large-Scale Bayesian Tensor Reconstruction via Approximate Message Passing

    arXiv:2505.16305v3 Announce Type: replace Abstract: While CANDECOMP/PARAFAC (CP) decomposition (CPD) is fundamental for tensor reconstruction, Bayesian CPD often scales poorly because variational updates require repeated matrix inversions. We develop CP generalized approximate me…

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