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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 501–510 of 823 papers

TitleStatusHype
LASERS: LAtent Space Encoding for Representations with Sparsity for Generative Modeling—0
Latent Dictionary Learning for Sparse Representation based Classification—0
Learned Multi-layer Residual Sparsifying Transform Model for Low-dose CT Reconstruction—0
Learning a collaborative multiscale dictionary based on robust empirical mode decomposition—0
Learning a Common Dictionary for CSI Feedback in FDD Massive MU-MIMO-OFDM Systems—0
Learning a Gaussian Mixture for Sparsity Regularization in Inverse Problems—0
Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization—0
Learning a Pedestrian Social Behavior Dictionary—0
Learning Based Segmentation of CT Brain Images: Application to Post-Operative Hydrocephalic Scans—0
Learning Better Encoding for Approximate Nearest Neighbor Search with Dictionary Annealing—0
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