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• W3149453403 abstract "In this paper we analyze the behavior of the Oja's algorithm for online/streaming principal component subspace estimation. It is proved that with high probability it performs an efficient, gap-free, global convergence rate to approximate an principal component subspace for any sub-Gaussian distribution. Moreover, it is the first time to show that the convergence rate, namely the upper bound of the approximation, exactly matches the lower bound of an approximation obtained by the offline/classical PCA up to a constant factor." @default.
• W3149453403 created "2021-04-13" @default.
• W3149453403 creator A5057011942 @default.
• W3149453403 date "2021-03-31" @default.
• W3149453403 modified "2023-09-24" @default.
• W3149453403 title "On the Optimality of the Oja's Algorithm for Online PCA" @default.
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• W3149453403 doi "https://doi.org/10.48550/arxiv.2104.00512" @default.
• W3149453403 hasPublicationYear "2021" @default.
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