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Dictionary Learning

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

Papers

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Showing 101–110 of 823 papers

TitleStatusHype
Global Identifiability of _1-based Dictionary Learning via Matrix Volume Optimization—0
Global Identifiability of _1-based Dictionary Learning via Matrix Volume Optimization—0
Multi-Source Domain Adaptation meets Dataset Distillation through Dataset Dictionary Learning—0
Federated Dataset Dictionary Learning for Multi-Source Domain Adaptation—0
Bayesian sparsity and class sparsity priors for dictionary learning and coding—0
Optimal Projections for Discriminative Dictionary Learning using the JL-lemmaCode0
Sparse Models for Machine Learning—0
Quantitative MR Image Reconstruction using Parameter-Specific Dictionary Learning with Adaptive Dictionary-Size and Sparsity-Level Choice—0
Exploring the Effect of Sparse Recovery on the Quality of Image Superresolution—0
On the Transition from Neural Representation to Symbolic Knowledge—0
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