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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

24 September 2019
Shusen Wang
ArXivPDFHTML

Papers citing "Simple and Almost Assumption-Free Out-of-Sample Bound for Random Feature Mapping"

4 / 4 papers shown
Title
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from
  Streaming Data
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data
J. Tropp
A. Yurtsever
Madeleine Udell
Volkan Cevher
41
81
0
18 Jun 2017
Scalable Kernel K-Means Clustering with Nystrom Approximation:
  Relative-Error Bounds
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds
Shusen Wang
Alex Gittens
Michael W. Mahoney
55
128
0
09 Jun 2017
Randomized Nonlinear Component Analysis
Randomized Nonlinear Component Analysis
David Lopez-Paz
S. Sra
Alex Smola
Zoubin Ghahramani
Bernhard Schölkopf
68
176
0
01 Feb 2014
Revisiting the Nystrom Method for Improved Large-Scale Machine Learning
Revisiting the Nystrom Method for Improved Large-Scale Machine Learning
Alex Gittens
Michael W. Mahoney
77
414
0
07 Mar 2013
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