EigenGame卸载：当玩游戏比优化更好
EigenGame Unloaded: When playing games is better than optimizing
摘要：
我们建立在最近提出的EigenGame，认为特征分解作为一个竞争性的游戏。如果使用小批量数据计算，EigenGame的更新会有偏差，这会阻碍随机设置中的收敛和更复杂的并行性。在这项工作中，我们提出了一个无偏的随机更新，是渐近等价的EigenGame，享有更大的并行性，允许更大的样本量的数据集上的计算，并在实验中优于EigenGame。我们目前的应用程序，以找到大规模数据集的主成分和执行谱聚类的图形。我们在EigenGame的背景下分析和讨论我们提出的更新，以及从优化到游戏的视角转变。
英文摘要：
We build on the recently proposed EigenGame that views eigendecomposition as a competitive game. EigenGame's updates are biased if computed using minibatches of data, which hinders convergence and more sophisticated parallelism in the stochastic setting. In this work, we propose an unbiased stochastic update that is asymptotically equivalent to EigenGame, enjoys greater parallelism allowing computation on datasets of larger sample sizes, and outperforms EigenGame in experiments. We present applications to finding the principal components of massive datasets and performing spectral clustering of graphs. We analyze and discuss our proposed update in the context of EigenGame and the shift in perspective from optimization to games.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
