Randomized sketches for kernel CCA
type: Item
instance of: scholarly article
title: Randomized sketches for kernel CCA
zbMATH Open document ID: 1472.62086
author: Heng Lian
author: F. Zhang
author: Wenqi Lu
published in: Neural Networks
publication date: 2021-09-08T00:00:00Z
Mathematics Subject Classification ID: 62H20
Mathematics Subject Classification ID: 62H12
zbMATH DE Number: 7392245
zbMATH Keywords: canonical correlation analysis (CCA)
zbMATH Keywords: covariance/cross-covariance operator
zbMATH Keywords: kernel method
zbMATH Keywords: random projection
MaRDI profile type: Publication
full work available at URL: j.neunet.2020.04.006
OpenAlex ID: W3016280609
Wikidata QID: Q92083273
cites work: Q4783930
cites work: 10.1162/153244303768966085
cites work: Divide and conquer in nonstandard problems and the super-efficiency phenomenon
cites work: A new randomized Kaczmarz based kernel canonical correlation analysis algorithm with applications to information retrieval
cites work: Minimax convergence rates for kernel CCA
cites work: Q3174083
cites work: Sparse canonical correlation analysis
cites work: RELATIONS BETWEEN TWO SETS OF VARIATES
cites work: Sparsity in multiple kernel learning
cites work: Canonical correlation analysis between time series and static outcomes, with application to the spectral analysis of heart rate variability
cites work: Q5361268
cites work: Multi-output parameter-insensitive kernel twin SVR model
cites work: Q5502137
cites work: Q4787374
cites work: Appearance models based on kernel canonical correlation analysis.
cites work: Q3046693
cites work: Randomized Sketches of Convex Programs With Sharp Guarantees
cites work: Iterative Hessian sketch: fast and accurate solution approximation for constrained least-squares
cites work: Minimax-optimal rates for sparse additive models over kernel classes via convex programming
cites work: A Massive Data Framework for M-Estimators with Cubic-Rate
cites work: Q3996207
cites work: Q4558555
cites work: Sketching meets random projection in the dual: a provable recovery algorithm for big and high-dimensional data
cites work: Information-Based Optimal Subdata Selection for Big Data Linear Regression
cites work: Optimal Subsampling for Large Sample Logistic Regression
cites work: Randomized sketches for kernels: fast and optimal nonparametric regression
cites work: Q2933949
cites work: Divide and conquer kernel ridge regression: a distributed algorithm with minimax optimal rates
cites work: A partially linear framework for massive heterogeneous data
DOI: 10.1016/J.NEUNET.2020.04.006
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description: scientific article; zbMATH DE number 7392245