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.

翻译：在本文中,我们分析了Oja的在线/流式主要组成部分子空间估计算法的行为。 事实证明,Oja的计算法极有可能实现高效的、无差距的、全球趋同率,以近似于任何亚高加索分布的主要组成部分子空间。 此外,这是第一次显示,近似值的上限的趋同率与离线/古典五氯苯甲醚获得的近似值的下限完全吻合,直至一个恒定系数。