14 citations · 17 across the 3 of their papers we have counts for
3 papers
Statistical Optimality and Computational Efficiency of Nyström Kernel PCA
Nicholas Sterge, Bharath Sriperumbudur
Kernel methods provide an elegant framework for developing nonlinear learning algorithms from simple linear methods. Though these methods have superior empirical performance in sev…
Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling
Nicholas Sterge, Bharath Sriperumbudur, Lorenzo Rosasco +1
In this paper, we propose and study a Nyström based approach to efficient large scale kernel principal component analysis (PCA). The latter is a natural nonlinear extension of clas…
Approximate Kernel PCA Using Random Features: Computational vs. Statistical Trade-off
Bharath Sriperumbudur, Nicholas Sterge
Kernel methods are powerful learning methodologies that allow to perform non-linear data analysis. Despite their popularity, they suffer from poor scalability in big data scenarios…