Large-scale kernel methods for independence testing

DOI10.1007/s11222-016-9721-7zbMath1384.62154arXiv1606.07892OpenAlexW2463154922MaRDI QIDQ1702289

Dino Sejdinovic, Qinyi Zhang, Arthur Gretton, Sarah Filippi

Publication date: 28 February 2018

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1606.07892

zbMATH Keywords

Nyström methodindependence testingHilbert-Schmidt independence criterialarge-scale kernel methodrandom Fourier features

Mathematics Subject Classification ID

Nonparametric hypothesis testing (62G10) Measures of association (correlation, canonical correlation, etc.) (62H20) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (12)

The Chi-Square Test of Distance Correlation ⋮ Unnamed Item ⋮ The Binary Expansion Randomized Ensemble Test ⋮ A Kernel Log-Rank Test of Independence for Right-Censored Data ⋮ Multivariate tests of independence based on a new class of measures of independence in reproducing kernel Hilbert space ⋮ Unnamed Item ⋮ A fast algorithm for computing distance correlation ⋮ A new coefficient of correlation ⋮ Validation of association ⋮ The exact equivalence of distance and kernel methods in hypothesis testing ⋮ Statistical dependence: beyond Pearson's \(\rho\) ⋮ Unnamed Item

Uses Software

• FastMMD

Cites Work

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• 10.1162/153244303768966085
• Support Vector Machines
• A Hilbert Space Embedding for Distributions
• FastMMD: Ensemble of Circular Discrepancy for Efficient Two-Sample Test
• Algorithmic Learning Theory
• Theory of Reproducing Kernels
• Scattered Data Approximation

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