无需调参的在线鲁棒PCA，靠梯度优化隐式正则实现
Tuning-Free Online Robust Principal Component Analysis through Implicit Regularization
- 用改进梯度下降的隐式正则化替代显式调参
- 在模拟和真实数据上性能不逊于调优版OR-PCA
- 适合大规模数据，免去依赖数据集的参数调优
标准在线鲁棒主成分分析（OR-PCA）的性能依赖于显式正则项的最优调参，且调参结果对数据集敏感。本文提出通过隐式正则化消除对这些调参参数的依赖。方法利用多种改进梯度下降的隐式正则效应，分别自然地促进数据中的稀疏性和低秩结构。所提方法在模拟与真实数据集上表现可媲美甚至优于调优后的OR-PCA。无调参的OR-PCA更适用于大规模数据，因其无需进行依赖数据集的参数调优。
原文摘要 · Abstract (English)
The performance of the standard Online Robust Principal Component Analysis (OR-PCA) technique depends on the optimum tuning of the explicit regularizers and this tuning is dataset sensitive. We aim to remove the dependency on these tuning parameters by using implicit regularization. We propose to use the implicit regularization effect of various modified gradient descents to make OR-PCA tuning free. Our method incorporates three different versions of modified gradient descent that separately but naturally encourage sparsity and low-rank structures in the data. The proposed method performs comparable or better than the tuned OR-PCA for both simulated and real-world datasets. Tuning-free ORPCA makes it more scalable for large datasets since we do not require dataset-dependent parameter tuning.
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