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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 151–160 of 823 papers

TitleStatusHype
Generalized Time Warping Invariant Dictionary Learning for Time Series Classification and Clustering—0
Interpretable Neural Embeddings with Sparse Self-Representation—0
SiBBlInGS: Similarity-driven Building-Block Inference using Graphs across StatesCode0
Complexity of Block Coordinate Descent with Proximal Regularization and Applications to Wasserstein CP-dictionary Learning—0
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary LearningCode0
Dictionary Learning under Symmetries via Group Representations—0
Personalized Dictionary Learning for Heterogeneous Datasets—0
Evolving Dictionary Representation for Few-shot Class-incremental Learning—0
Learning Dictionaries from Physical-Based Interpolation for Water Network Leak LocalizationCode0
Toward Real-Time Image Annotation Using Marginalized Coupled Dictionary LearningCode0
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