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Dictionary learning is a classic representation learning method that has been widely applied in signal processing and data analytics.
ℓ p \ell_{p} -norm deconvolution
HWJ Debeye and P Van Riel · 1990
Earlier work this paper cites.
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Bruno A Olshausen and David J Field · 1996
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A fast fixed-point algorithm for independent component analysis
Aapo Hyvärinen and Erkki Oja · 1997
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K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation
Michal Aharon, Michael Elad, and Alfred Bruckstein · 2006
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Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit
Ron Rubinstein, Michael Zibulevsky, and Michael Elad · 2008
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Optimization algorithms on matrix manifolds
P-A Absil, Robert Mahony, and Rodolphe Sepulchre · 2009
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Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
M. Elad · 2010
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Generalized power method for sparse principal component analysis
Michel Journée, Yurii Nesterov, Peter Richtárik, and Rodolphe Sepulchre · 2010
Earlier work this paper cites.
Robust principal component analysis?
Emmanuel J Candès, Xiaodong Li, Yi Ma, and John Wright · 2011
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Exact recovery of sparsely-used dictionaries
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When are nonconvex problems not scary?
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René Vidal, Yi Ma, and S Shankar Sastry · 2016
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Roman Vershynin · 2018
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Subgradient descent learns orthogonal dictionaries
Yu Bai, Qijia Jiang, and Ju Sun · 2019
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Efficient dictionary learning with gradient descent
Dar Gilboa, Sam Buchanan, and John Wright · 2019
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Complete dictionary learning via ℓ 4 \ell_{4} -norm maximization over the orthogonal group
Yi Ma · 2019
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Complete dictionary learning via ℓ 4 \ell_{4} -norm maximization over the orthogonal group
Yuexiang Zhai, Zitong Yang, Zhenyu Liao, John Wright, and Yi Ma · 2019
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Geometric analysis of nonconvex optimization landscapes for overcomplete learning
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Global geometry of multichannel sparse blind deconvolution on the sphere
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