Communication Efficient Distributed Kernel Principal Component Analysis
Maria Florina Balcan, Yingyu Liang, Le Song, David P. Woodruff, Bo Xie · 2016
Kernel Principal Component Analysis (KPCA) is a key machine learning algorithm for extracting nonlinear features from data. In the presence of a large volume of high dimensional data collected in a distributed fashion, it becomes very costly to communicate all of this data to a single data center and then perform kernel PCA. Can we perform kernel PCA on the entire dataset in a distributed and communication efficient fashion while maintaining provable and strong guarantees in solution quality?