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Sparse dictionary learning

Sparse dictionary learning is a representation learning method which aims at finding a sparse representation of the input data (also known as sparse… Expand
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Papers overview

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Highly Cited
2018
Highly Cited
2018
The recently proposed multilayer convolutional sparse coding (ML-CSC) model, consisting of a cascade of convolutional sparse… Expand
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Highly Cited
2016
Highly Cited
2016
Sparse representation has shown to be a very powerful model for real world signals, and has enabled the development of… Expand
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Highly Cited
2016
Highly Cited
2016
Is it possible to find the sparsest vector (direction) in a generic subspace S ⊆ ℝp with dim(S) = n <; p? This problem can be… Expand
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Highly Cited
2015
Highly Cited
2015
A popular approach within the signal processing and machine learning communities consists in modeling signals as sparse linear… Expand
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Highly Cited
2013
Highly Cited
2013
Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately… Expand
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Highly Cited
2013
Highly Cited
2013
This paper studies the visual tracking problem in video sequences and presents a novel robust sparse tracker under the particle… Expand
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Highly Cited
2010
Highly Cited
2010
We investigate fast methods that allow to quickly eliminate variables (features) in supervised learning problems involving a… Expand
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Highly Cited
2009
Highly Cited
2009
We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are… Expand
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Highly Cited
2008
Highly Cited
2008
It is now well established that sparse signal models are well suited for restoration tasks and can be effectively learned from… Expand
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Highly Cited
2008
Highly Cited
2008
We present a convex formulation of dictionary learning for sparse signal decomposition. Convexity is obtained by replacing the… Expand
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