Online Learning for Matrix Factorization and Sparse Coding
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Summary
A new online optimization algorithm is proposed, based on stochastic approximations, which scales up gracefully to large data sets with millions of training samples, and extends naturally to various matrix factorization formulations, making it suitable for a wide range of learning problems.
- Type
- article
- Published
- 2009-08-01
- Cited by
- 2,703
- References
- 95
- Access
- Open access
- OpenAlex
- https://openalex.org/W2112447569
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:556331
Keywords
Computer science, Matrix decomposition, Neural coding, Artificial intelligence, Sparse matrix
References
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- Addendum: Regularization and variable selection via the elastic net
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- Optimization Problems with Perturbations: A Guided Tour
- Convex Analysis and Nonlinear Optimization: Theory and Examples. Jonathan M. Borwein and Adrian S. Lewis, Springer, New York, 2000
- Online dictionary learning for sparse coding
- Sparse Modeling of Textures
- Sparse and Redundant Modeling of Image Content Using an Image-Signature-Dictionary
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- Pig Brains Have Homologous Resting-State Networks with Human Brains
- Proximal Methods for Sparse Hierarchical Dictionary Learning
- Splitting Algorithms for Convex Optimization and Applications to Sparse Matrix Factorization
- Kernel Coding: General Formulation and Special Cases
- Sparse coding for machine learning, image processing and computer vision
- Visual Indexing and Retrieval
- Advances in spectral learning with applications to text analysis and brain imaging
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