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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 751–760 of 823 papers

TitleStatusHype
Supervised Dictionary Learning by a Variational Bayesian Group Sparse Nonnegative Matrix Factorization—0
On the Computational Intractability of Exact and Approximate Dictionary Learning—0
Group-based Sparse Representation for Image RestorationCode0
Anomaly-Sensitive Dictionary Learning for Unsupervised Diagnostics of Solid Media—0
On The Sample Complexity of Sparse Dictionary Learning—0
A Split-and-Merge Dictionary Learning Algorithm for Sparse Representation—0
Collaborative Representation for Classification, Sparse or Non-sparse?—0
Group sparsity and geometry constrained dictionary learning for action recognition from depth maps.—0
An Incidence Geometry approach to Dictionary Learning—0
Performance Limits of Dictionary Learning for Sparse Coding—0
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