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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 691–700 of 823 papers

TitleStatusHype
Super-Resolution Person Re-Identification With Semi-Coupled Low-Rank Discriminant Dictionary Learning—0
Multi-task additive models with shared transfer functions based on dictionary learning—0
Local identifiability of l_1-minimization dictionary learning: a sufficient and almost necessary condition—0
Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?—0
Complete Dictionary Recovery over the SphereCode0
A Generative Model for Deep Convolutional Learning—0
Efficient Dictionary Learning via Very Sparse Random Projections—0
Discriminative Bayesian Dictionary Learning for Classification—0
Convergence radius and sample complexity of ITKM algorithms for dictionary learningCode0
On some provably correct cases of variational inference for topic models—0
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