The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Online dictionary learning for sparse coding
Open accessAbstract
Sparse coding---that is, modelling data vectors as sparse linear combinations of basis elements---is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on learning the basis set, also called dictionary, to adapt it to specific data, an approach that has recently proven to be very effective for signal reconstruction and classification in the audio and image processing domains. This paper proposes a new online optimization algorithm for dictionary learning, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples. A proof of convergence is presented, along with experiments with natural images demonstrating that it leads to faster performance and better dictionaries than classical batch algorithms for both small and large datasets.
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Metadata
- Authors
- Mairal, J., Bach, F., Ponce, J., & Sapiro, G.
- Venue
- Proceedings of the 26th Annual International Conference on Machine Learning
- Publisher
- ACM
- Published
- 2009-06-14
- Pages
- 689-696
- OpenAlex
- W2005876975
- Cited by
- 2,114
- References
- 5
- FWCI
- 138.59
- Type
- conference paper
- Language
- en
- Open access
- green
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