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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 391–400 of 823 papers

TitleStatusHype
Deep Interpretable Non-Rigid Structure from MotionCode0
NOODL: Provable Online Dictionary Learning and Sparse Coding—0
Unique Sharp Local Minimum in _1-minimization Complete Dictionary Learning—0
Convolutional Dictionary Regularizers for Tomographic Inversion—0
Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold—0
Dictionary learning approach to monitoring of wind turbine drivetrain bearings—0
TGAN: Deep Tensor Generative Adversarial Nets for Large Image GenerationCode0
Distributed Convolutional Dictionary Learning (DiCoDiLe): Pattern Discovery in Large Images and SignalsCode0
On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution—0
Automated Multiscale 3D Feature Learning for Vessels Segmentation in Thorax CT Images—0
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