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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 381–390 of 823 papers

TitleStatusHype
Identifying global optimality for dictionary learning—0
Image Deraining via Self-supervised Reinforcement Learning—0
Image patch analysis of sunspots and active regions. I. Intrinsic dimension and correlation analysis—0
Deep learning based dictionary learning and tomographic image reconstruction—0
Interpretable Neural Embeddings with Sparse Self-Representation—0
Improving Language Models Trained on Translated Data with Continual Pre-Training and Dictionary Learning Analysis—0
Discriminative Feature and Dictionary Learning with Part-aware Model for Vehicle Re-identification—0
Impulse Denoising From Hyper-Spectral Images: A Blind Compressed Sensing Approach—0
Deep Multi-Resolution Dictionary Learning for Histopathology Image Analysis—0
Discriminative Dictionary Learning based on Statistical Methods—0
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