Approximate kernel PCA: computational versus statistical trade-off
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Publication:2105193
DOI10.1214/22-AOS2204MaRDI QIDQ2105193
Nicholas Sterge, Bharath K. Sriperumbudur
Publication date: 8 December 2022
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1706.06296
principal component analysisreproducing kernel Hilbert spacecovariance operatorBernstein's inequalitykernel PCArandom feature approximation
Factor analysis and principal components; correspondence analysis (62H25) Nonparametric estimation (62G05)
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Cites Work
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- Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
- Kernel PCA for novelty detection
- Randomized sketches for kernels: fast and optimal nonparametric regression
- Approximate kernel PCA: computational versus statistical trade-off
- Statistical properties of kernel principal component analysis
- Optimal rates for the regularized least-squares algorithm
- Some results on Tchebycheffian spline functions and stochastic processes
- 10.1162/15324430260185619
- Support Vector Machines
- On the Eigenspectrum of the Gram Matrix and the Generalization Error of Kernel-PCA
- Adaptive KPCA Modeling of Nonlinear Systems
- Measure Theory
- Theory of Reproducing Kernels
- Scattered Data Approximation
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