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Approximate Kernel PCA Using Random Features: Computational vs.
  Statistical Trade-off
v1v2v3v4 (latest)

Approximate Kernel PCA Using Random Features: Computational vs. Statistical Trade-off

20 June 2017
Bharath K. Sriperumbudur
Nicholas Sterge
ArXiv (abs)PDFHTML

Papers citing "Approximate Kernel PCA Using Random Features: Computational vs. Statistical Trade-off"

14 / 14 papers shown
HyperFast: Instant Classification for Tabular Data
HyperFast: Instant Classification for Tabular Data
David Bonet
D. M. Montserrat
Xavier Giró-i-Nieto
A. Ioannidis
281
28
0
22 Feb 2024
Spectral Regularized Kernel Goodness-of-Fit Tests
Spectral Regularized Kernel Goodness-of-Fit Tests
Omar Hagrass
Bharath K. Sriperumbudur
Bing Li
411
5
0
08 Aug 2023
Spectral Regularized Kernel Two-Sample Tests
Spectral Regularized Kernel Two-Sample TestsAnnals of Statistics (Ann. Stat.), 2022
Omar Hagrass
Bharath K. Sriperumbudur
Bing Li
364
18
0
19 Dec 2022
Contrastive Learning Can Find An Optimal Basis For Approximately
  View-Invariant Functions
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant FunctionsInternational Conference on Learning Representations (ICLR), 2022
Daniel D. Johnson
Ayoub El Hanchi
Chris J. Maddison
SSL
335
31
0
04 Oct 2022
Statistical and Computational Trade-offs in Variational Inference: A
  Case Study in Inferential Model Selection
Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection
Kush S. Bhatia
Nikki Lijing Kuang
Yi-An Ma
Yixin Wang
246
8
0
22 Jul 2022
Kernel PCA with the Nyström method
Kernel PCA with the Nyström method
Fredrik Hallgren
298
3
0
12 Sep 2021
Statistical Optimality and Computational Efficiency of Nyström Kernel
  PCA
Statistical Optimality and Computational Efficiency of Nyström Kernel PCAJournal of machine learning research (JMLR), 2021
Nicholas Sterge
Bharath K. Sriperumbudur
306
18
0
19 May 2021
Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature
  Mapping
Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping
Shusen Wang
225
2
0
24 Sep 2019
Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling
Gain with no Pain: Efficient Kernel-PCA by Nyström SamplingInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Nicholas Sterge
Bharath K. Sriperumbudur
Lorenzo Rosasco
Alessandro Rudi
306
8
0
11 Jul 2019
On Kernel Derivative Approximation with Random Fourier Features
On Kernel Derivative Approximation with Random Fourier Features
Z. Szabó
Bharath K. Sriperumbudur
358
13
0
11 Oct 2018
Data-dependent compression of random features for large-scale kernel
  approximation
Data-dependent compression of random features for large-scale kernel approximation
Raj Agrawal
Trevor Campbell
Jonathan H. Huggins
Tamara Broderick
216
20
0
09 Oct 2018
Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features
Streaming Kernel PCA with O~(n)\tilde{O}(\sqrt{n})O~(n​) Random Features
Enayat Ullah
Poorya Mianjy
T. V. Marinov
R. Arora
283
22
0
02 Aug 2018
Optimal Rates of Sketched-regularized Algorithms for Least-Squares
  Regression over Hilbert Spaces
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert SpacesInternational Conference on Machine Learning (ICML), 2018
Junhong Lin
Volkan Cevher
159
9
0
12 Mar 2018
Compressive Statistical Learning with Random Feature Moments
Compressive Statistical Learning with Random Feature Moments
Rémi Gribonval
Gilles Blanchard
Nicolas Keriven
Y. Traonmilin
387
54
0
22 Jun 2017
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