A Variational Method for Learning Sparse and Overcomplete Representations

DOI10.1162/089976601753196003zbMath0993.68082OpenAlexW2161195301WikidataQ52128254 ScholiaQ52128254MaRDI QIDQ2784819

Mark A. Girolami

Publication date: 24 April 2002

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/089976601753196003

zbMATH Keywords

learningexpectation-maximization algorithm

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computing methodologies for image processing (68U10)

Related Items (10)

Deep Learning--Based Dictionary Learning and Tomographic Image Reconstruction ⋮ Analysis of Sparse Representation and Blind Source Separation ⋮ Bayesian Approaches to Shrinkage and Sparse Estimation ⋮ Unnamed Item ⋮ Evaluation of generalized degrees of freedom for sparse estimation by replica method ⋮ Iterative estimation algorithms using conjugate function lower bound and minorization-maximization with applications in image denoising ⋮ Dictionary Learning Algorithms for Sparse Representation ⋮ A Note on Lewicki-Sejnowski Gradient for Learning Overcomplete Representations ⋮ K-hyperline clustering learning for sparse component analysis ⋮ A null space method for over-complete blind source separation

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