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

Keywords

Computer science, Matrix decomposition, Neural coding, Artificial intelligence, Sparse matrix

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