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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 81–90 of 823 papers

TitleStatusHype
A Batchwise Monotone Algorithm for Dictionary Learning—0
Active Deep Learning for Classification of Hyperspectral Images—0
Beyond Codebook-Based Analog Beamforming at mmWave: Compressed Sensing and Machine Learning Methods—0
Beyond Label Attention: Transparency in Language Models for Automated Medical Coding via Dictionary Learning—0
Automatic Identification of Epileptic Seizures from EEG Signals using Sparse Representation-based Classification—0
Automated Multiscale 3D Feature Learning for Vessels Segmentation in Thorax CT Images—0
A variational autoencoder-based nonnegative matrix factorisation model for deep dictionary learning—0
Analysis Dictionary Learning: An Efficient and Discriminative Solution—0
A Fast Dictionary Learning Method for Coupled Feature Space Learning—0
Analysis Co-Sparse Coding for Energy Disaggregation—0
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