Skip to search formSkip to main content
You are currently offline. Some features of the site may not work correctly.

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
Wikipedia

Related topics

Related topics

27 relations
Audio signal processingAutoencoderBasis (linear algebra)Conjugate gradient method
Expand

Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2018
Highly Cited
2018
Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning
The recently proposed multilayer convolutional sparse coding (ML-CSC) model, consisting of a cascade of convolutional sparse… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2016
Highly Cited
2016
Trainlets: Dictionary Learning in High Dimensions
Sparse representation has shown to be a very powerful model for real world signals, and has enabled the development of… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2016
Highly Cited
2016
Finding a Sparse Vector in a Subspace: Linear Sparsity Using Alternating Directions
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
  • table 1
  • figure 1
  • figure 2
  • figure 3
  • figure 4
Is this relevant?
Highly Cited
2015
Highly Cited
2015
Sparse and Spurious: Dictionary Learning With Noise and Outliers
A popular approach within the signal processing and machine learning communities consists in modeling signals as sparse linear… Expand
  • table I
  • figure 1
Is this relevant?
Highly Cited
2013
Highly Cited
2013
Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution
Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately… Expand
  • figure 2
  • figure 1
  • table 1
  • figure 3
  • table 3
Is this relevant?
Highly Cited
2013
Highly Cited
2013
Online Robust Non-negative Dictionary Learning for Visual Tracking
This paper studies the visual tracking problem in video sequences and presents a novel robust sparse tracker under the particle… Expand
  • figure 1
  • figure 2
  • table 2
  • table 1
  • figure 3
Is this relevant?
Highly Cited
2010
Highly Cited
2010
Safe Feature Elimination in Sparse Supervised Learning
We investigate fast methods that allow to quickly eliminate variables (features) in supervised learning problems involving a… Expand
  • table 1
  • figure 1
  • figure 2
  • figure 3
  • figure 4
Is this relevant?
Highly Cited
2009
Highly Cited
2009
Structured Sparse Principal Component Analysis
We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
Is this relevant?
Highly Cited
2008
Highly Cited
2008
Supervised Dictionary Learning
It is now well established that sparse signal models are well suited for restoration tasks and can be effectively learned from… Expand
  • figure 1
  • table 1
  • figure 3
  • table 2
  • figure 4
Is this relevant?
Highly Cited
2008
Highly Cited
2008
Convex Sparse Matrix Factorizations
We present a convex formulation of dictionary learning for sparse signal decomposition. Convexity is obtained by replacing the… Expand
  • table 1
Is this relevant?
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License
ACCEPT & CONTINUE