论文标题
带有RBF内核的PCA的单发算法算法
One-shot Distibuted Algorithm for PCA with RBF Kernels
论文作者
论文摘要
这封信为特征分布的核PCA提出了一种单发算法。我们的算法灵感来自样本分布和特征分布的方案之间的双重关系。这种有趣的关系使得可以从分布式PCA中的特征分布式案例中建立分布式内核PCA，以样本分布的方案中的分布式pca。在理论部分，我们分析了线性和RBF内核的近似误差。结果表明，当特征值快速衰减时，拟议的算法给出了高质量的结果，而沟通成本较低。数值实验还验证了该结果，显示了我们算法在实践中的有效性。
This letter proposes a one-shot algorithm for feature-distributed kernel PCA. Our algorithm is inspired by the dual relationship between sample-distributed and feature-distributed scenario. This interesting relationship makes it possible to establish distributed kernel PCA for feature-distributed cases from ideas in distributed PCA in sample-distributed scenario. In theoretical part, we analyze the approximation error for both linear and RBF kernels. The result suggests that when eigenvalues decay fast, the proposed algorithm gives high quality results with low communication cost. This result is also verified by numerical experiments, showing the effectiveness of our algorithm in practice.
