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Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features

Streaming Kernel PCA with O~(n)\tilde{O}(\sqrt{n})O~(n​) Random Features

2 August 2018
Enayat Ullah
Poorya Mianjy
T. V. Marinov
R. Arora
ArXivPDFHTML

Papers citing "Streaming Kernel PCA with $\tilde{O}(\sqrt{n})$ Random Features"

12 / 12 papers shown
Title
Towards a Scalable Reference-Free Evaluation of Generative Models
Towards a Scalable Reference-Free Evaluation of Generative Models
Azim Ospanov
Jingwei Zhang
Mohammad Jalali
Xuenan Cao
Andrej Bogdanov
Farzan Farnia
EGVM
32
1
0
03 Jul 2024
Fair Streaming Principal Component Analysis: Statistical and Algorithmic
  Viewpoint
Fair Streaming Principal Component Analysis: Statistical and Algorithmic Viewpoint
Junghyun Lee
Hanseul Cho
Se-Young Yun
Chulhee Yun
30
5
0
28 Oct 2023
Contrastive Learning Can Find An Optimal Basis For Approximately
  View-Invariant Functions
Contrastive Learning Can Find An Optimal Basis For Approximately View-Invariant Functions
Daniel D. Johnson
Ayoub El Hanchi
Chris J. Maddison
SSL
11
22
0
04 Oct 2022
Learning to Forecast Dynamical Systems from Streaming Data
Learning to Forecast Dynamical Systems from Streaming Data
D. Giannakis
Amelia Henriksen
J. Tropp
Rachel A. Ward
AI4TS
33
17
0
20 Sep 2021
Statistical Optimality and Computational Efficiency of Nyström Kernel
  PCA
Statistical Optimality and Computational Efficiency of Nyström Kernel PCA
Nicholas Sterge
Bharath K. Sriperumbudur
15
8
0
19 May 2021
Generalization Bounds for Sparse Random Feature Expansions
Generalization Bounds for Sparse Random Feature Expansions
Abolfazl Hashemi
Hayden Schaeffer
Robert Shi
Ufuk Topcu
Giang Tran
Rachel A. Ward
MLT
32
40
0
04 Mar 2021
Random Features for Kernel Approximation: A Survey on Algorithms,
  Theory, and Beyond
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond
Fanghui Liu
Xiaolin Huang
Yudong Chen
Johan A. K. Suykens
BDL
27
172
0
23 Apr 2020
Learning with Optimized Random Features: Exponential Speedup by Quantum
  Machine Learning without Sparsity and Low-Rank Assumptions
Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions
H. Yamasaki
Sathyawageeswar Subramanian
Sho Sonoda
M. Koashi
15
17
0
22 Apr 2020
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
21
2
0
24 Sep 2019
Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling
Gain with no Pain: Efficient Kernel-PCA by Nyström Sampling
Nicholas Sterge
Bharath K. Sriperumbudur
Lorenzo Rosasco
Alessandro Rudi
6
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
11
12
0
11 Oct 2018
Compressive Statistical Learning with Random Feature Moments
Compressive Statistical Learning with Random Feature Moments
Rémi Gribonval
Gilles Blanchard
Nicolas Keriven
Y. Traonmilin
14
49
0
22 Jun 2017
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