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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 451–475 of 823 papers

TitleStatusHype
On the uniqueness and stability of dictionaries for sparse representation of noisy signals—0
Optimal Regularization for a Data Source—0
Optimal Spectral Recovery of a Planted Vector in a Subspace—0
Optimization of Clustering for Clustering-based Image Denoising—0
Optimized Kernel-based Projection Space of Riemannian Manifolds—0
Optimizing Hard Thresholding for Sparse Model Discovery—0
Overcomplete Dictionary Learning with Jacobi Atom Updates—0
Patchwise Sparse Dictionary Learning from pre-trained Neural Network Activation Maps for Anomaly Detection in Images—0
Per-Block-Convex Data Modeling by Accelerated Stochastic Approximation—0
Performance Limits of Dictionary Learning for Sparse Coding—0
Permutation-invariant Feature Restructuring for Correlation-aware Image Set-based Recognition—0
Personalized Age Progression with Aging Dictionary—0
Personalized Age Progression with Bi-level Aging Dictionary Learning—0
Personalized Dictionary Learning for Heterogeneous Datasets—0
Person Re-Identification With Discriminatively Trained Viewpoint Invariant Dictionaries—0
PET Image Reconstruction with Multiple Kernels and Multiple Kernel Space Regularizers—0
Phase transitions and sample complexity in Bayes-optimal matrix factorization—0
Poisson noise reduction with non-local PCA—0
Prior-Less Compressible Structure From Motion—0
Probabilistic Forecasting and Simulation of Electricity Markets via Online Dictionary Learning—0
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)—0
Proceedings of the third "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'16)—0
Projective dictionary pair learning for pattern classification—0
ProSper -- A Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions—0
Provable Online Dictionary Learning and Sparse Coding—0
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