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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 651–675 of 823 papers

TitleStatusHype
Adaptively Unified Semi-Supervised Dictionary Learning With Active Points—0
Subset Selection by Pareto Optimization—0
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) - The _0 Method—0
Multimodal sparse representation learning and applications—0
Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse ProblemsCode1
FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning and Applications—0
Sparse-promoting Full Waveform Inversion based on Online Orthonormal Dictionary Learning—0
Complete Dictionary Recovery over the Sphere II: Recovery by Riemannian Trust-region Method—0
Jointly Learning Non-negative Projection and Dictionary with Discriminative Graph Constraints for Classification—0
Zero-Shot Learning via Joint Latent Similarity Embedding—0
Complete Dictionary Recovery over the Sphere I: Overview and the Geometric Picture—0
Multiple Instance Dictionary Learning using Functions of Multiple InstancesCode0
Computational Intractability of Dictionary Learning for Sparse Representation—0
Personalized Age Progression with Aging Dictionary—0
When Are Nonconvex Problems Not Scary?Code0
Linearized Kernel Dictionary LearningCode0
Overcomplete Dictionary Learning with Jacobi Atom Updates—0
Dictionary Learning and Sparse Coding for Third-order Super-symmetric Tensors—0
Extractive Summarization by Maximizing Semantic Volume—0
Dictionary Learning for Blind One Bit Compressed Sensing—0
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
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