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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 671–680 of 823 papers

TitleStatusHype
A Dictionary Learning Approach for Factorial Gaussian Models—0
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy—0
Dictionary and Image Recovery from Incomplete and Random Measurements—0
When can dictionary learning uniquely recover sparse data from subsamples?—0
Making sense of randomness: an approach for fast recovery of compressively sensed signals—0
On the Minimax Risk of Dictionary Learning—0
Multiscale Adaptive Representation of Signals: I. The Basic Framework—0
Riemannian Dictionary Learning and Sparse Coding for Positive Definite Matrices—0
Learning Better Encoding for Approximate Nearest Neighbor Search with Dictionary Annealing—0
Flexible Multi-layer Sparse Approximations of Matrices and Applications—0
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