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New algorithm estimates principal eigenvector with minimal data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a compressed version of Oja's algorithm for estimating the principal eigenvector of a data covariance matrix. This method requires only two adaptive measurements per sample, significantly reducing data acquisition needs. The analysis proves that the expected sine-squared error to the true eigenvector is bounded, establishing a theoretical limit for compressed eigenvector estimation and demonstrating its efficiency compared to non-adaptive schemes. AI

RANK_REASON The cluster contains an academic paper detailing a new algorithm and its theoretical analysis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

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

New algorithm estimates principal eigenvector with minimal data

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The cluster contains an academic paper detailing a new algorithm and its theoretical analysis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Alex Saad-Falcon, Brighton Ancelin, Justin Romberg ·

    Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation

    arXiv:2505.10882v2 Announce Type: replace-cross Abstract: Principal component analysis classically requires full $d$-dimensional samples, yet in various applications hardware limits acquisition to a few scalar measurements per sample. We analyze a compressed variant of Oja's algo…

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