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]
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