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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Cites
Regression shrinkage and selection via the lasso
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Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
Elad, M., & Aharon, M. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881969 · Cited by 5,321
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Sparse coding with an overcomplete basis set: A strategy employed by V1?
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Sparse Representation for Color Image Restoration
Mairal, J., Elad, M., & Sapiro, G. (2008) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.911828 · Cited by 1,716
Self-citation
Cited by
Deep learning for visual understanding: A review
Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.09.116 · Cited by 2,504
Medical Image Denoising Using Convolutional Denoising Autoencoders
Gondara, L. (2016) · 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw.2016.0041 · Cited by 737
Machine learning in acoustics: Theory and applications
Bianco, M. J., Gerstoft, P., Traer, J., Ozanich, E., Roch, M. A., Gannot, S., & Deledalle, C.-A. (2019) · The Journal of the Acoustical Society of America · doi:10.1121/1.5133944 · Cited by 609
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
Levi, G., & Hassner, T. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2830587 · Cited by 336
Sparse coding based visual tracking: Review and experimental comparison
Zhang, S., Yao, H., Sun, X., & Lu, X. (2013) · Pattern Recognition · doi:10.1016/j.patcog.2012.10.006 · Cited by 284
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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