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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 426–450 of 823 papers

TitleStatusHype
On Learning Sparsely Used Dictionaries from Incomplete Samples—0
Online Convolutional Dictionary Learning—0
Online Convolutional Dictionary Learning for Multimodal Imaging—0
Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems—0
Online Dictionary Learning for Approximate Archetypal Analysis—0
Online dictionary learning for kernel LMS. Analysis and forward-backward splitting algorithm—0
Online L1-Dictionary Learning with Application to Novel Document Detection—0
Online Low-Rank Subspace Learning from Incomplete Data: A Bayesian View—0
Online multidimensional dictionary learning—0
Online Multilinear Dictionary Learning—0
Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning—0
Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method—0
Online Robust Dictionary Learning—0
Online Unsupervised Feature Learning for Visual Tracking—0
On some provably correct cases of variational inference for topic models—0
On the Analysis of Multi-Channel Neural Spike Data—0
On the Computational Intractability of Exact and Approximate Dictionary Learning—0
On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution—0
On the Invariance of Dictionary Learning and Sparse Representation to Projecting Data to a Discriminative Space—0
On the Minimax Risk of Dictionary Learning—0
On the Preservation of Spatio-temporal Information in Machine Learning Applications—0
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions—0
On the relations of LFPs & Neural Spike Trains—0
On The Sample Complexity of Sparse Dictionary Learning—0
On the Transition from Neural Representation to Symbolic Knowledge—0
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