Dictionary Learning
153 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
Latest papers
Anomaly Detection with Selective Dictionary Learning
In this paper we present new methods of anomaly detection based on Dictionary Learning (DL) and Kernel Dictionary Learning (KDL).
Classification with Incoherent Kernel Dictionary Learning
In this paper we present a new classification method based on Dictionary Learning (DL).
Reduced Kernel Dictionary Learning
In this paper we present new algorithms for training reduced-size nonlinear representations in the Kernel Dictionary Learning (KDL) problem.
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across States
Time series data across scientific domains are often collected under distinct states (e. g., tasks), wherein latent processes (e. g., biological factors) create complex inter- and intra-state variability.
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary Learning
Nonlocal self-similarity within natural images has become an increasingly popular prior in deep-learning models.
Learning Dictionaries from Physical-Based Interpolation for Water Network Leak Localization
This article presents a leak localization methodology based on state estimation and learning.
Toward Real-Time Image Annotation Using Marginalized Coupled Dictionary Learning
We have employed a marginalized loss function in our method to leverage a simple and effective method of prototype updating.
Cloud K-SVD for Image Denoising
Cloud K-SVD is a dictionary learning algorithm that can train at multiple nodes and hereby produce a mutual dictionary to represent low-dimensional geometric structures in image data.
Hiding Data Helps: On the Benefits of Masking for Sparse Coding
2014mchidamb/masked-sparse-coding-icml
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Furthermore, drawing from the growing body of work on self-supervised learning, we propose a novel masking objective for which recovering the ground-truth dictionary is in fact optimal as the signal increases for a large class of data-generating processes.
An Efficient Approximate Method for Online Convolutional Dictionary Learning
Most existing convolutional dictionary learning (CDL) algorithms are based on batch learning, where the dictionary filters and the convolutional sparse representations are optimized in an alternating manner using a training dataset.
