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This paper considers the problem of estimating the principal eigenvector of a covariance matrix from independent and identically distributed data samples in streaming settings.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
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TP Krasulina · 1969
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Sashank J Reddi, Suvrit Sra, Barnabás Póczos, and Alex Smola · 1977
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On stochastic approximation of the eigenvectors and eigenvalues of the expectation of a random matrix
Erkki Oja and Juha Karhunen · 1985
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Elad Hazan, Kfir Yehuda Levy, and Shai Shalev-Shwartz · 2016
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Streaming PCA: Matching matrix bernstein and near-optimal finite sample guarantees for oja’s algorithm
Prateek Jain, Chi Jin, Sham M Kakade, Praneeth Netrapalli, and Aaron Sidford · 2016
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Rivalry of two families of algorithms for memory-restricted streaming PCA
Chun-Liang Li, Hsuan-Tien Lin, and Chi-Jen Lu · 2016
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Jiazhong Nie, Wojciech Kotlowski, and Manfred K Warmuth · 2016
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Fast stochastic algorithms for SVD and PCA: Convergence properties and convexity
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On the computational inefficiency of large batch sizes for stochastic gradient descent
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An implicit form of Krasulina’s k-PCA update without the orthonormality constraint
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