Accurate Low-Rank Approximations Via a Few Iterations of Alternating Least Squares

DOI10.1137/16M1064556zbMath1367.65067OpenAlexW2619181281MaRDI QIDQ5270414

Mark Tygert, Andrew Tulloch, Arthur D. Szlam

Publication date: 23 June 2017

Published in: SIAM Journal on Matrix Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/16m1064556

zbMATH Keywords

convergenceprincipal component analysisrandomized algorithmlow-rank approximationalternating least squaresalternating minimizationsubspace iterations

Mathematics Subject Classification ID

Numerical solutions to overdetermined systems, pseudoinverses (65F20) Iterative numerical methods for linear systems (65F10)

Related Items (4)

Efficient alternating least squares algorithms for low multilinear rank approximation of tensors ⋮ Stable Rank-Adaptive Dynamically Orthogonal Runge–Kutta Schemes ⋮ Regularized Linear Inversion with Randomized Singular Value Decomposition ⋮ Regression-aware decompositions

Cites Work

• Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions
• The principal components of mixed measurement level multivariate data: An alternating least squares method with optimal scaling features
• Randomized algorithms for low-rank matrix factorizations: sharp performance bounds
• Computational Advertising: Techniques for Targeting Relevant Ads
• A Randomized Algorithm for Principal Component Analysis
• Subspace Iteration Randomization and Singular Value Problems

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