Book contents
- Frontmatter
- Contents
- Preface
- Nomenclature
- Part I Formulation
- Part II Algorithm
- Part III Nonasymptotic Theory
- 6 Global VB Solution of Fully Observed Matrix Factorization
- 7 Model-Induced Regularization and Sparsity Inducing Mechanism
- 8 Performance Analysis of VB Matrix Factorization
- 9 Global Solver for Matrix Factorization
- 10 Global Solver for Low-Rank Subspace Clustering
- 11 Efficient Solver for Sparse Additive Matrix Factorization
- 12 MAP and Partially Bayesian Learning
- Part IV Asymptotic Theory
- Appendix A James–Stein Estimator
- Appendix B Metric in Parameter Space
- Appendix C Detailed Description of Overlap Method
- Appendix D Optimality of Bayesian Learning
- Bibliography
- Index
8 - Performance Analysis of VB Matrix Factorization
from Part III - Nonasymptotic Theory
Published online by Cambridge University Press: 24 June 2019
Book contents
- Frontmatter
- Contents
- Preface
- Nomenclature
- Part I Formulation
- Part II Algorithm
- Part III Nonasymptotic Theory
- 6 Global VB Solution of Fully Observed Matrix Factorization
- 7 Model-Induced Regularization and Sparsity Inducing Mechanism
- 8 Performance Analysis of VB Matrix Factorization
- 9 Global Solver for Matrix Factorization
- 10 Global Solver for Low-Rank Subspace Clustering
- 11 Efficient Solver for Sparse Additive Matrix Factorization
- 12 MAP and Partially Bayesian Learning
- Part IV Asymptotic Theory
- Appendix A James–Stein Estimator
- Appendix B Metric in Parameter Space
- Appendix C Detailed Description of Overlap Method
- Appendix D Optimality of Bayesian Learning
- Bibliography
- Index
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- Variational Bayesian Learning Theory , pp. 205 - 235Publisher: Cambridge University PressPrint publication year: 2019