An approximate linear solver in least square support vector machine using randomized singular value decomposition

DOI10.1007/s11859-015-1094-9zbMath1340.65065OpenAlexW2281838364MaRDI QIDQ3461640

Bing Liu, Hua Xiang

Publication date: 15 January 2016

Published in: Wuhan University Journal of Natural Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11859-015-1094-9

zbMATH Keywords

machine learningNyström methodLanczos processleast square support vector machinerandomized singular value decompositionlarge-scale data regression

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Numerical computation of eigenvalues and eigenvectors of matrices (65F15) Numerical solutions to overdetermined systems, pseudoinverses (65F20) Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

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• Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
• Low rank updated LS-SVM classifiers for fast variable selection
• Regularization with randomized SVD for large-scale discrete inverse problems
• The Lanczos Algorithm with Selective Orthogonalization
• Chaos control using least-squares support vector machines
• Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis
• A tutorial on ν‐support vector machines