Numerical study of learning algorithms on Stiefel manifold

DOI10.1007/s10287-013-0181-7zbMath1331.68184OpenAlexW2019205337WikidataQ125847531 ScholiaQ125847531MaRDI QIDQ2355188

Akiko Takeda, Takafumi Kanamori

Publication date: 21 July 2015

Published in: Computational Management Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10287-013-0181-7

zbMATH Keywords

Stiefel manifoldnon-convex optimizationdimensionality reductionalternating direction method of multipliers

Mathematics Subject Classification ID

Convex programming (90C25) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (2)

AN EFFICIENT METHOD FOR SOLVING A CLASS OF MATRIX TRACE FUNCTION MINIMIZATION PROBLEM IN MULTIVARIATE STATISTICAL ⋮ Alternating direction method of multipliers for truss topology optimization with limited number of nodes: a cardinality-constrained second-order cone programming approach

Uses Software

• R
• PRMLT

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Statistical analysis of kernel-based least-squares density-ratio estimation
• Direct density-ratio estimation with dimensionality reduction via least-squares hetero-distributional subspace search
• Linear and nonlinear programming.
• Density Ratio Estimation in Machine Learning
• Robust Data Mining
• The Geometry of Algorithms with Orthogonality Constraints
• A Unified Classification Model Based on Robust Optimization
• The elements of statistical learning. Data mining, inference, and prediction

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