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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 511–520 of 823 papers

TitleStatusHype
Learning brain regions via large-scale online structured sparse dictionary learning—0
Learning Class Prototypes via Structure Alignment for Zero-Shot Recognition—0
Learning computationally efficient dictionaries and their implementation as fast transforms—0
Learning Deep Analysis Dictionaries -- Part II: Convolutional Dictionaries—0
Learning differentiable solvers for systems with hard constraints—0
Learning Discriminative ab-Divergences for Positive Definite Matrices—0
Learning Discriminative Alpha-Beta-divergence for Positive Definite Matrices (Extended Version)—0
Learning Discriminative Latent Attributes for Zero-Shot Classification—0
Learning efficient structured dictionary for image classification—0
Learning _1-based analysis and synthesis sparsity priors using bi-level optimization—0
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