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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 741–750 of 823 papers

TitleStatusHype
A Fully Automated Latent Fingerprint Matcher with Embedded Self-learning Segmentation Module—0
Learning computationally efficient dictionaries and their implementation as fast transforms—0
Structured Dictionary Learning for Classification—0
Learning Scalable Discriminative Dictionary with Sample Relatedness—0
Latent Dictionary Learning for Sparse Representation based Classification—0
l0 Norm Based Dictionary Learning by Proximal Methods with Global Convergence—0
Semi-Supervised Coupled Dictionary Learning for Person Re-identification—0
Multivariate General Linear Models (MGLM) on Riemannian Manifolds with Applications to Statistical Analysis of Diffusion Weighted Images—0
Decomposable Nonlocal Tensor Dictionary Learning for Multispectral Image Denoising—0
Sparse Dictionary Learning for Edit Propagation of High-Resolution Images—0
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