A random-order streaming algorithm approximates the top eigenvector with near-linear memory whenever the spectral gap is constant, and a lower bound shows the heavy-row parameter is unavoidable.
Optimal principal component analysis in distributed and streaming models
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Approximating the Top Eigenvector in Random Order Streams
A random-order streaming algorithm approximates the top eigenvector with near-linear memory whenever the spectral gap is constant, and a lower bound shows the heavy-row parameter is unavoidable.