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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 341–350 of 823 papers

TitleStatusHype
Functional connectome fingerprinting: Identifying individuals and predicting cognitive function via deep learning—0
Dataset Dictionary Learning in a Wasserstein Space for Federated Domain Adaptation—0
Applications of Online Nonnegative Matrix Factorization to Image and Time-Series Data—0
Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering—0
Compressive Sensing and Neural Networks from a Statistical Learning Perspective—0
Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networks—0
Generalized Adaptive Dictionary Learning via Domain Shift Minimization—0
Discriminative Feature and Dictionary Learning with Part-aware Model for Vehicle Re-identification—0
Generalized Conditional Gradient for Sparse Estimation—0
Discriminative Dictionary Learning based on Statistical Methods—0
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