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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

Showing 601625 of 823 papers

TitleStatusHype
NOODL: Provable Online Dictionary Learning and Sparse Coding0
Novel min-max reformulations of Linear Inverse Problems0
Uncovering Model Processing Strategies with Non-Negative Per-Example Fisher Factorization0
Object Classification with Joint Projection and Low-rank Dictionary Learning0
Observable dictionary learning for high-dimensional statistical inference0
OnACID: Online Analysis of Calcium Imaging Data in Real Time0
On Learning Sparsely Used Dictionaries from Incomplete Samples0
Online Convolutional Dictionary Learning0
Online Convolutional Dictionary Learning for Multimodal Imaging0
Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems0
Online Dictionary Learning for Approximate Archetypal Analysis0
Online dictionary learning for kernel LMS. Analysis and forward-backward splitting algorithm0
Online L1-Dictionary Learning with Application to Novel Document Detection0
Online Low-Rank Subspace Learning from Incomplete Data: A Bayesian View0
Online multidimensional dictionary learning0
Online Multilinear Dictionary Learning0
Online Multi-Source Domain Adaptation through Gaussian Mixtures and Dataset Dictionary Learning0
Online Orthogonal Dictionary Learning Based on Frank-Wolfe Method0
Online Robust Dictionary Learning0
Online Unsupervised Feature Learning for Visual Tracking0
On some provably correct cases of variational inference for topic models0
On the Analysis of Multi-Channel Neural Spike Data0
On the Computational Intractability of Exact and Approximate Dictionary Learning0
On the Global Geometry of Sphere-Constrained Sparse Blind Deconvolution0
On the Invariance of Dictionary Learning and Sparse Representation to Projecting Data to a Discriminative Space0
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