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Sparse approximation

Known as: Sparse optimization, Sparse representation 
A sparse approximation is a sparse vector that approximately solves a system of equations. Techniques for finding sparse approximations have found… 
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Related topics

Related topics

24 relations
Ali AkansuBasis pursuitBasis pursuit denoisingCombinatorial optimization

Broader (1)

Numerical linear algebra

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2018
2018
Multiple Feature Kernel Sparse Representation Classifier for Hyperspectral Imagery
Multiple types of features, e.g., spectral, filtering, texture, and shape features, are helpful for hyperspectral image (HSI… 
2015
2015
Sparse representation for frequency warping based voice conversion
This paper presents a sparse representation framework for weighted frequency warping based voice conversion. In this method, a… 
2014
2014
Replicating Kernels with a Short Stride Allows Sparse Reconstructions with Fewer Independent Kernels
In sparse coding it is common to tile an image into nonoverlapping patches, and then use a dictionary to create a sparse… 
2012
2012
Modular Weighted Global Sparse Representation for Robust Face Recognition
This work proposes a novel framework of robust face recognition based on the sparse representation. Image is first divided into… 
2011
2011
Speaker Verification Using Sparse Representations on Total Variability i-vectors
In this paper, the sparse representation computed by lminimization with quadratic constraints is employed to model the i-vectors… 
Highly Cited
2011
Highly Cited
2011
Minimum Error Bounded Efficient L1 Tracker with Occlusion Detection (PREPRINT)
Abstract : Recently, sparse representation has been applied to visual tracking to find the target with the minimum reconstruction… 
2010
2010
Learning Sparse Representation Using Iterative Subspace Identification
In this paper, we introduce the iterative subspace identification (ISI) algorithm for learning subspaces in which the data may… 
Highly Cited
2007
Highly Cited
2007
SparSpec: a new method for fitting multiple sinusoids with irregularly sampled data
Context. The location of pure frequencies in the spectrum of an irregularly sampled time series is an important topic in… 
Review
2006
Review
2006
Learning Sparse Overcomplete Codes for Images
Images can be coded accurately using a sparse set of vectors from a learned overcomplete dictionary, with potential applications… 
Highly Cited
2006
Highly Cited
2006
MR Image Reconstruction from Sparse Radial Samples Using Bregman Iteration
2 1 || ) ( || || ) ( || || || L k L BV v y f NFFT f f − − + Ψ + λ υ , where vk=y+ vk-1-NFFT(fk) with the convention v0=0, to… 
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