Dictionary Learning
152 papers with code • 0 benchmarks • 6 datasets
Dictionary Learning is an important problem in multiple areas, ranging from computational neuroscience, machine learning, to computer vision and image processing. The general goal is to find a good basis for given data. More formally, in the Dictionary Learning problem, also known as sparse coding, we are given samples of a random vector $y\in\mathbb{R}^n$, of the form $y=Ax$ where $A$ is some unknown matrix in $\mathbb{R}^{n×m}$, called dictionary, and $x$ is sampled from an unknown distribution over sparse vectors. The goal is to approximately recover the dictionary $A$.
Source: Polynomial-time tensor decompositions with sum-of-squares
Benchmarks
These leaderboards are used to track progress in Dictionary Learning
Libraries
Use these libraries to find Dictionary Learning models and implementations
Extended Yale B
Partial-REID
SALSA
FMD
Partial-iLIDS
LoDoPaB-CT
Most implemented papers
Discovery of Nonlinear Dynamical Systems using a Runge-Kutta Inspired Dictionary-based Sparse Regression Approach
Discovering dynamical models to describe underlying dynamical behavior is essential to draw decisive conclusions and engineering studies, e. g., optimizing a process.
Subtle Data Crimes: Naively training machine learning algorithms could lead to overly-optimistic results
mikgroup/subtle_data_crimes
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We demonstrate this phenomenon for inverse problem solvers and show how their biased performance stems from hidden data preprocessing pipelines.
Fusing Dictionary Learning and Support Vector Machines for Unsupervised Anomaly Detection
We introduce a new anomaly detection model that unifies the OC-SVM and DL residual functions into a single composite objective, subsequently solved through K-SVD-type iterative algorithms.
Metrics for Multivariate Dictionaries
Despite a recurrent need to rely on a distance for learning or assessing multivariate overcomplete representations, no metrics in their underlying spaces have yet been proposed.
Sparse Coding and Dictionary Learning for Symmetric Positive Definite Matrices: A Kernel Approach
Recent advances suggest that a wide range of computer vision problems can be addressed more appropriately by considering non-Euclidean geometry.
Learning parametric dictionaries for graph signals
In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties -- the ability to adapt to specific signal data and a fast implementation of the dictionary.
Group-based Sparse Representation for Image Restoration
In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal patches with similar structures, and establish a novel sparse representation modeling of natural images, called group-based sparse representation (GSR).
Subspace metrics for multivariate dictionaries and application to EEG
Overcomplete representations and dictionary learning algorithms are attracting a growing interest in the machine learning community.
On the need for metrics in dictionary learning assessment
Dictionary-based approaches are the focus of a growing attention in the signal processing community, often achieving state of the art results in several application fields.
Finding a sparse vector in a subspace: Linear sparsity using alternating directions
In this paper, we focus on a **planted sparse model** for the subspace: the target sparse vector is embedded in an otherwise random subspace.
